Les formations Intelligence Artificielle
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Plan des Formations
| Code | Nom | Durée | Aperçu |
|---|---|---|---|
| brmsdrools | Business Rule Management (BRMS) with Drools | 7 heures | This course is aimed at enterprise architects, business and system analysts and managers who want to apply business rules to their solution. With Drools you can write your business rules using almost natural language, therefore reducing the gap between business and IT. |
| appliedml | Applied Machine Learning | 14 heures | This training course is for people that would like to apply Machine Learning in practical applications. Audience This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience. |
| datamin | Data Mining | 21 heures | Course can be provided with any tools, including free open-source data mining software and applications |
| hadoopadm1 | Hadoop pour Administrateurs | 21 heures | Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. In this three (optionally, four) days course, attendees will learn about the business benefits and use cases for Hadoop and its ecosystem, how to plan cluster deployment and growth, how to install, maintain, monitor, troubleshoot and optimize Hadoop. They will also practice cluster bulk data load, get familiar with various Hadoop distributions, and practice installing and managing Hadoop ecosystem tools. The course finishes off with discussion of securing cluster with Kerberos. “…The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized” — Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising Audience Hadoop administrators Format Lectures and hands-on labs, approximate balance 60% lectures, 40% labs. |
| bspkannmldt | Réseau de Neurones Artificiels, Machine Learning et Deep Thinking | 21 heures | Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML. |
| w2vdl4j | NLP with Deeplearning4j | 14 heures | Deeplearning4j is an open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs. Word2Vec is a method of computing vector representations of words introduced by a team of researchers at Google led by Tomas Mikolov. Audience This course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models. |
| datamodeling | Pattern Recognition | 35 heures | This course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired. Audience Data analysts PhD students, researchers and practitioners |
| BigData_ | A Practical Introduction to Data Analysis and Big Data | 35 heures | Participants who complete this training will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools. Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class. The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability. Audience Developers / programmers IT consultants Format of the course Part lecture, part discussion, hands-on practice and implementation, occasional quizing to measure progress |
| matlabdsandreporting | MATLAB Fundamentals, Data Science & Report Generation | 126 heures | In the first part of this training, we cover the fundamentals of MATLAB and its function as both a language and a platform. Included in this discussion is an introduction to MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented principles. In the second part, we demonstrate how to use MATLAB for data mining, machine learning and predictive analytics. To provide participants with a clear and practical perspective of MATLAB's approach and power, we draw comparisons between using MATLAB and using other tools such as spreadsheets, C, C++, and Visual Basic. In the third part of the training, participants learn how to streamline their work by automating their data processing and report generation. Throughout the course, participants will put into practice the ideas learned through hands-on exercises in a lab environment. By the end of the training, participants will have a thorough grasp of MATLAB's capabilities and will be able to employ it for solving real-world data science problems as well as for streamlining their work through automation. Assessments will be conducted throughout the course to gauge progress. Format of the course Course includes theoretical and practical exercises, including case discussions, sample code inspection, and hands-on implementation. Note Practice sessions will be based on pre-arranged sample data report templates. If you have specific requirements, please contact us to arrange. |
| jupyter | Jupyter for Data Science Teams | 7 heures | Jupyter is an open-source, web-based interactive IDE and computing environment. This instructor-led, live training introduces the idea of collaborative development in data science and demonstrates how to use Jupyter to track and participate as a team in the "life cycle of a computational idea". It walks participants through the creation of a sample data science project based on top of the Jupyter ecosystem. By the end of this training, participants will be able to: Install and configure Jupyter, including the creation and integration of a team repository on Git Use Jupyter features such as extensions, interactive widgets, multiuser mode and more to enable project collaboraton Create, share and organize Jupyter Notebooks with team members Choose from Scala, Python, R, to write and execute code against big data systems such as Apache Spark, all through the Jupyter interface Audience Data science teams Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note The Jupypter Notebook supports over 40 languages including R, Python, Scala, Julia, etc. To customize this course to your language(s) of choice, please contact us to arrange. |
| encogintro | Encog: Introduction to Machine Learning | 14 heures | Encog is an open-source machine learning framework for Java and .Net. In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored. By the end of this training, participants will be able to: Prepare data for neural networks using the normalization process Implement feed forward networks and propagation training methodologies Implement classification and regression tasks Model and train neural networks using Encog's GUI based workbench Integrate neural network support into real-world applications Audience Developers Analysts Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| opennlp | OpenNLP for Text Based Machine Learning | 14 heures | The Apache OpenNLP library is a machine learning based toolkit for processing natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution. In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises. By the end of this training, participants will be able to: Install and configure OpenNLP Download existing models as well as create their own Train the models on various sets of sample data Integrate OpenNLP with existing Java applications Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| pythoncomputervision | Computer Vision with Python | 14 heures | Computer Vision is a field that involves automatically extracting, analyzing, and understanding useful information from digital media. Python is a high-level programming language famous for its clear syntax and code readibility. In this instructor-led, live training, participants will learn the basics of Computer Vision as they step through the creation of set of simple Computer Vision application using Python. By the end of this training, participants will be able to: Understand the basics of Computer Vision Use Python to implement Computer Vision tasks Build their own face, object, and motion detection systems Audience Python programmers interested in Computer Vision Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| dlformedicine | Deep Learning for Medicine | 14 heures | Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep Learning is a subfield of Machine Learning which attempts to mimic the workings of the human brain in making decisions. It is trained with data in order to automatically provide solutions to problems. Deep Learning provides vast opportunities for the medical industry which is sitting on a data goldmine. In this instructor-led, live training, participants will take part in a series of discussions, exercises and case-study analysis to understand the fundamentals of Deep Learning. The most important Deep Learning tools and techniques will be evaluated and exercises will be carried out to prepare participants for carrying out their own evaluation and implementation of Deep Learning solutions within their organizations. By the end of this training, participants will be able to: Understand the fundamentals of Deep Learning Learn Deep Learning techniques and their applications in the industry Examine issues in medicine which can be solved by Deep Learning technologies Explore Deep Learning case studies in medicine Formulate a strategy for adopting the latest technologies in Deep Learning for solving problems in medicine Audience Managers Medical professionals in leadership roles Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note To request a customized training for this course, please contact us to arrange. |
| aiint | Artificial Intelligence Overview | 7 heures | This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development. |
| mlintro | Introduction to Machine Learning | 7 heures | This training course is for people that would like to apply basic Machine Learning techniques in practical applications. Audience Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work Sector specific examples are used to make the training relevant to the audience. |
| apacheh | Administrator Training for Apache Hadoop | 35 heures | Audience: The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment Goal: Deep knowledge on Hadoop cluster administration. |
| solrdev | Solr pour Développeurs | 21 heures | This course introduces students to the Solr platform. Through a combination of lecture, discussion and labs students will gain hands on experience configuring effective search and indexing. The class begins with basic Solr installation and configuration then teaches the attendees the search features of Solr. Students will gain experience with faceting, indexing and search relevance among other features central to the Solr platform. The course wraps up with a number of advanced topics including spell checking, suggestions, Multicore and SolrCloud. Duration: 3 days Audience: Developers, business users, administrators |
| osovv | OpenStack Overview | 7 heures | The course is dedicated to IT engineers and architects who are looking for a solution to host private or public IaaS (Infrastructure as a Service) cloud. This is also great opportunity for IT managers to gain knowledge overview about possibilities which could be enabled by OpenStack. Before You spend a lot of money on OpenStack implementation, You could consider all pros and cons by attending on our course. This topic is also avaliable as individual consultancy. Course goal: gaining basic knowledge regarding OpenStack |
| tsflw2v | Natural Language Processing with TensorFlow | 35 heures | TensorFlow™ is an open source software library for numerical computation using data flow graphs. SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow. Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.). Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input. Audience This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs. After completing this course, delegates will: understand TensorFlow’s structure and deployment mechanisms be able to carry out installation / production environment / architecture tasks and configuration be able to assess code quality, perform debugging, monitoring be able to implement advanced production like training models, embedding terms, building graphs and logging |
| processmining | Process Mining | 21 heures | Process mining, or Automated Business Process Discovery (ABPD), is a technique that applies algorithms to event logs for the purpose of analyzing business processes. Process mining goes beyond data storage and data analysis; it bridges data with processes and provides insights into the trends and patterns that affect process efficiency. Format of the course The course starts with an overview of the most commonly used techniques for process mining. We discuss the various process discovery algorithms and tools used for discovering and modeling processes based on raw event data. Real-life case studies are examined and data sets are analyzed using the ProM open-source framework. Audience Data science professionals Anyone interested in understanding and applying process modeling and data mining |
| octnp | Octave not only for programmers | 21 heures | Course is dedicated for those who would like to know an alternative program to the commercial MATLAB package. The three-day training provides comprehensive information on moving around the environment and performing the OCTAVE package for data analysis and engineering calculations. The training recipients are beginners but also those who know the program and would like to systematize their knowledge and improve their skills. Knowledge of other programming languages is not required, but it will greatly facilitate the learners' acquisition of knowledge. The course will show you how to use the program in many practical examples. |
| tpuprogramming | TPU Programming: Building Neural Network Applications on Tensor Processing Units | 7 heures | The Tensor Processing Unit (TPU) is the architecture which Google has used internally for several years, and is just now becoming available for use by the general public. It includes several optimizations specifically for use in neural networks, including streamlined matrix multiplication, and 8-bit integers instead of 16-bit in order to return appropriate levels of precision. In this instructor-led, live training, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications. By the end of the training, participants will be able to: Train various types of neural networks on large amounts of data Use TPUs to speed up the inference process by up to two orders of magnitude Utilize TPUs to process intensive applications such as image search, cloud vision and photos Audience Developers Researchers Engineers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| datameer | Datameer for Data Analysts | 14 heures | Datameer is a business intelligence and analytics platform built on Hadoop. It allows end-users to access, explore and correlate large-scale, structured, semi-structured and unstructured data in an easy-to-use fashion. In this instructor-led, live training, participants will learn how to use Datameer to overcome Hadoop's steep learning curve as they step through the setup and analysis of a series of big data sources. By the end of this training, participants will be able to: Create, curate, and interactively explore an enterprise data lake Access business intelligence data warehouses, transactional databases and other analytic stores Use a spreadsheet user-interface to design end-to-end data processing pipelines Access pre-built functions to explore complex data relationships Use drag-and-drop wizards to visualize data and create dashboards Use tables, charts, graphs, and maps to analyze query results Audience Data analysts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| pythontextml | Python: Machine Learning with Text | 21 heures | In this instructor-led, live training, participants will learn how to use the right machine learning and NLP (Natural Language Processing) techniques to extract value from text-based data. By the end of this training, participants will be able to: Solve text-based data science problems with high-quality, reusable code Apply different aspects of scikit-learn (classification, clustering, regression, dimensionality reduction) to solve problems Build effective machine learning models using text-based data Create a dataset and extract features from unstructured text Visualize data with Matplotlib Build and evaluate models to gain insight Troubleshoot text encoding errors Audience Developers Data Scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| undnn | Understanding Deep Neural Networks | 35 heures | This course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications). Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc. Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy. Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow. Audience This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects After completing this course, delegates will: have a good understanding on deep neural networks(DNN), CNN and RNN understand TensorFlow’s structure and deployment mechanisms be able to carry out installation / production environment / architecture tasks and configuration be able to assess code quality, perform debugging, monitoring be able to implement advanced production like training models, building graphs and logging Not all the topics would be covered in a public classroom with 35 hours duration due to the vastness of the subject. The Duration of the complete course will be around 70 hours and not 35 hours. |
| mlfinancer | Machine Learning for Finance (with R) | 28 heures | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. By the end of this training, participants will be able to: Understand the fundamental concepts in machine learning Learn the applications and uses of machine learning in finance Develop their own algorithmic trading strategy using machine learning with R Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| bigdataM | Big Data and its Management Process | 14 heures | Objective : This training course aims at helping attendees understand why Big Data is changing our lives and how it is altering the way businesses see us as consumers. Indeed, users of big data in businesses find that big data unleashes a wealth of information and insights which translate to higher profits, reduced costs, and less risk. However, the downside was frustration sometimes when putting too much emphasis on individual technologies and not enough focus on the pillars of big data management. Attendees will learn during this course how to manage the big data using its three pillars of data integration, data governance and data security in order to turn big data into real business value. Different exercices conducted on a case study of customer management will help attendees to better understand the underlying processes. |
| smtwebint | Semantic Web Overview | 7 heures | The Semantic Web is a collaborative movement led by the World Wide Web Consortium (W3C) that promotes common formats for data on the World Wide Web. The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. |
| manbrphp | Managing Business Rules with PHP Business Rules | 14 heures | This course explain how to write declarative rules using PHP Business Rules (http://sourceforge.net/projects/phprules/). It shows how to write, organize and integrate rules with existing code. Most of the course is based on exercises preceded with short introduction and examples. |
| hadoopadm | Administration de Hadoop | 21 heures | The course is dedicated to IT specialists that are looking for a solution to store and process large data sets in distributed system environment Course goal: Getting knowledge regarding Hadoop cluster administration |
| sparkdev | Spark for Developers | 21 heures | OBJECTIVE: This course will introduce Apache Spark. The students will learn how Spark fits into the Big Data ecosystem, and how to use Spark for data analysis. The course covers Spark shell for interactive data analysis, Spark internals, Spark APIs, Spark SQL, Spark streaming, and machine learning and graphX. AUDIENCE : Developers / Data Analysts |
| bspkaml | Machine Learning | 21 heures | This course will be a combination of theory and practical work with specific examples used throughout the event. |
| opencv | Vision par ordinateur avec OpenCV | 28 heures | OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source BSD-licensed library that includes several hundreds of computer vision algorithms. Audience This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects |
| patternmatching | Pattern Matching | 14 heures | Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not. Audience Engineers and developers seeking to develop machine vision applications Manufacturing engineers, technicians and managers Format of the course This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision. |
| mlentre | Machine Learning Concepts for Entrepreneurs and Managers | 21 heures | This training course is for people that would like to apply Machine Learning in practical applications for their team. The training will not dive into technicalities and revolve around basic concepts and business/operational applications of the same. Target Audience Investors and AI entrepreneurs Managers and Engineers whose company is venturing into AI space Business Analysts & Investors |
| TalendDI | Talend Open Studio for Data Integration | 28 heures | Talend Open Studio for Data Integration is an open-source data integration product used to combine, convert and update data in various locations across a business. In this instructor-led, live training, participants will learn how to use the Talend ETL tool to carry out data transformation, data extraction, and connectivity with Hadoop, Hive, and Pig. By the end of this training, participants will be able to Explain the concepts behind ETL (Extract, Transform, Load) and propagation Define ETL methods and ETL tools to connect with Hadoop Efficiently amass, retrieve, digest, consume, transform and shape big data in accordance to business requirements Upload to and extract large records from Hadoop, Hive, and NoSQL databases Audience Business intelligence professionals Project managers Database professionals SQL Developers ETL Developers Solution architects Data architects Data warehousing professionals System administrators and integrators Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| dsstne | Amazon DSSTNE: Build a recommendation system | 7 heures | Amazon DSSTNE is an open-source library for training and deploying recommendation models. It allows models with weight matrices that are too large for a single GPU to be trained on a single host. In this instructor-led, live training, participants will learn how to use DSSTNE to build a recommendation application. By the end of this training, participants will be able to: Train a recommendation model with sparse datasets as input Scale training and prediction models over multiple GPUs Spread out computation and storage in a model-parallel fashion Generate Amazon-like personalized product recommendations Deploy a production-ready application that can scale at heavy workloads Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| flockdb | Flockdb: A Simple Graph Database for Social Media | 7 heures | FlockDB is an open source distributed, fault-tolerant graph database for managing wide but shallow network graphs. It was initially used by Twitter to store relationships among users. In this instructor-led, live training, participants will learn how to setup and use a FlockDB database to help answer social media questions such as who follows whom, who blocks whom, etc. By the end of this training, participants will be able to: Install and configure FlockDB Understand the unique features of FlockDB, relative to other graph databases such Neo4j Use FlockDB to maintain a large graph dataset Use FlockDB together with MySQL to provide provide distributed storage capabilities Query, create and update extremely fast graph edges Scale FlockDB horizontally for use in on-line, low-latency, high throughput web environments Audience Developers Database engineers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| d3js | D3.js for Data Visualization | 7 heures | D3.js (or D3 for Data-Driven Documents) is a JavaScript library that uses SVG, HTML5, and CSS for producing dynamic, interactive data visualizations in web browsers. In this instructor-led, live training, participants will learn how to create web-based data-driven visualizations that run on multiple devices responsively. By the end of this training, participants will be able to: Use D3 to create interactive graphics, information dashboards, infographics and maps Control HTML with jQuery-like selections Transform the DOM by selecting elements and joining to data Export SVG for use in print publications Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| dlfinancewithr | Deep Learning for Finance (with R) | 28 heures | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in finance Use R to create deep learning models for finance Build their own deep learning stock price prediction model using R Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| sparksql | Apache Spark SQL | 7 heures | Spark SQL is Apache Spark's module for working with structured and unstructured data. Spark SQL provides information about the structure of the data as well as the computation being performed. This information can be used to perform optimizations. Two common uses for Spark SQL are: - to execute SQL queries. - to read data from an existing Hive installation. In this instructor-led, live training (onsite or remote), participants will learn how to analyze various types of data sets using Spark SQL. By the end of this training, participants will be able to: Install and configure Spark SQL. Perform data analysis using Spark SQL. Query data sets in different formats. Visualize data and query results. Audience Data analysts Data scientists Data engineers Format of the Course Part lecture, part discussion, exercises and heavy hands-on practice Notes To request a customized training for this course, please contact us to arrange. |
| drools6int | Introduction à Drools 6 pour Développeurs | 21 heures | This 3 days course is aimed to introduce Drools 6 to developers.This course doesn't cover drools integration, performance or any other complex topics. |
| bigdatar | Programming with Big Data in R | 21 heures | Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry. |
| annmldt | Artificial Neural Networks, Machine Learning, Deep Thinking | 21 heures | Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML. |
| hbasedev | HBase for Developers | 21 heures | This course introduces HBase – a NoSQL store on top of Hadoop. The course is intended for developers who will be using HBase to develop applications, and administrators who will manage HBase clusters. We will walk a developer through HBase architecture and data modelling and application development on HBase. It will also discuss using MapReduce with HBase, and some administration topics, related to performance optimization. The course is very hands-on with lots of lab exercises. Duration : 3 days Audience : Developers & Administrators |
| datashrinkgov | Data Shrinkage for Government | 14 heures | The objective of the course is to enable participants to gain a mastery of the fundamentals of data shrinkage for government. |
| intror | Introduction to R with Time Series Analysis | 21 heures | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining. |
| kdd | Knowledge Discover in Databases (KDD) | 21 heures | Knowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. Real-life applications for this data mining technique include marketing, fraud detection, telecommunication and manufacturing. In this course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes. Audience Data analysts or anyone interested in learning how to interpret data to solve problems Format of the course After a theoretical discussion of KDD, the instructor will present real-life cases which call for the application of KDD to solve a problem. Participants will prepare, select and cleanse sample data sets and use their prior knowledge about the data to propose solutions based on the results of their observations. |
| teraintro | Teradata Fundamentals | 21 heures | Teradata is one of the popular Relational Database Management System. It is mainly suitable for building large scale data warehousing applications. Teradata achieves this by the concept of parallelism. This course introduces the delegates to Teradata |
| pythonmultipurpose | Advanced Python | 28 heures | In this instructor-led training, participants will learn advanced Python programming techniques, including how to apply this versatile language to solve problems in areas such as distributed applications, data analysis and visualization, UI programming and maintenance scripting. Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Notes If you wish to add, remove or customize any section or topic within this course, please contact us to arrange. |
| cognitivecomputing | Cognitive Computing: An Introduction for Business Managers | 7 heures | Cognitive computing refers to systems that encompass machine learning, reasoning, natural language processing, speech recognition and vision (object recognition), human–computer interaction, dialog and narrative generation, to name a few. A cognitive computing system is often comprised of multiple technologies working together to process in-memory ‘hot’ contextual data as well as large sets of ‘cold’ historical data in batch. Examples of such technologies include Kafka, Spark, Elasticsearch, Cassandra and Hadoop. In this instructor-led, live training, participants will learn how Cognitive Computing compliments AI and Big Data and how purpose-built systems can be used to realize human-like behaviors that improve the performance of human-machine interactions in business. By the end of this training, participants will understand: The relationship between cognitive computing and artificial intelligence (AI) The inherently probabilistic nature of cognitive computing and how to use it as a business advantage How to manage cognitive computing systems that behave in unexpected ways Which companies and software systems offer the most compelling cognitive computing solutions Audience Business managers Format of the course Lecture, case discussions and exercises |
| datavisualizationreports | Data Visualization: Creating Captivating Reports | 21 heures | In this instructor-led, live training, participants will learn the skills, strategies, tools and approaches for visualizing and reporting data for different audiences. Case studies are also analyzed and discussed to exemplify how data visualization solutions are being applied in the real world to derive meaning out of data and answer crucial questions. By the end of this training, participants will be able to: Write reports with captivating titles, subtitles, and annotations using the most suitable highlighting, alignment, and color schemes for readability and user friendliness. Design charts that fit the audience's information needs and interests Choose the best chart types for a given dataset (beyond pie charts and bar charts) Identify and analyze the most valuable and relevant data quickly and efficiently Select the best file formats to include in reports (graphs, infographics, references, GIFs, etc.) Create effective layouts for displaying time series data, part-to-whole relationships, geographic patterns, and nested data Use effective color-coding to display qualitative and text-based data such as sentiment analysis, timelines, calendars, and diagrams Apply the most suitable tools for the job (Excel, R, Tableau, mapping programs, etc.) Prepare datasets for visualization Audience Data analysts Business managers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| highcharts | Highcharts for Data Visualization | 7 heures | Highcharts is an open-source JavaScript library for creating interactive graphical charts on the Web. It is commonly used to represent data in a more user-readable and interactive fashion. In this instructor-led, live training, participants will learn how to create high-quality data visualizations for web applications using Highcharts. By the end of this training, participants will be able to: Set up interactive charts on the Web using only HTML and JavaScript Represent large datasets in visually interesting and interactive ways Export charts to JPEG, PNG, SVG, or PDF Integrate Highcharts with jQuery Mobile for cross-platform compatibility Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| dlforbankingwithpython | Deep Learning for Banking (with Python) | 28 heures | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in banking Use Python, Keras, and TensorFlow to create deep learning models for banking Build their own deep learning credit risk model using Python Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| introtostreamprocessing | A Practical Introduction to Stream Processing | 21 heures | Stream Processing refers to the real-time processing of "data in motion", that is, performing computations on data as it is being received. Such data is read as continuous streams from data sources such as sensor events, website user activity, financial trades, credit card swipes, click streams, etc. Stream Processing frameworks are able to read large volumes of incoming data and provide valuable insights almost instantaneously. In this instructor-led, live training (onsite or remote), participants will learn how to setup and integrate different Stream Processing frameworks with existing big data storage systems and related software applications and microservices. By the end of this training, participants will be able to: Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming Understand and select the most appropriate framework for the job Process of data continuously, concurrently, and in a record-by-record fashion Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc. Integrating the most appropriate stream processing library with enterprise applications and microservices Audience Developers Software architects Format of the Course Part lecture, part discussion, exercises and heavy hands-on practice Notes To request a customized training for this course, please contact us to arrange. |
| neuralnet | Introduction à l'utilisation de Réseau Neuronal | 7 heures | The training is aimed at people who want to learn the basics of neural networks and their applications. |
| psr | Introduction to Recommendation Systems | 7 heures | Audience Marketing department employees, IT strategists and other people involved in decisions related to the design and implementation of recommender systems. Format Short theoretical background follow by analysing working examples and short, simple exercises. |
| dsguihtml5jsre | Designing Inteligent User Interface with HTML5, JavaScript and Rule Engines | 21 heures | Coding interfaces which allow users to get what they want easily is hard. This course guides you how to create effective UI with newest technologies and libraries. It introduces idea of coding logic in Rule Engines (mostly Nools and PHP Rules) to make it easier to modify and test. After that the courses shows a way of integrating the logic on the front end of the website using JavaScript. Logic coded this way can be reused on the backend. |
| droolsdslba | Drools 6 and DSL for Business Analysts | 21 heures | This 3 days course is aimed to introduce Drools 6 to Business Analysts responsible for writing tests and rules. This course focuses on creating pure logic. Analysts after this course can writing tests and logic which then can be further integrated by developers with business applications. |
| predio | Machine Learning avec PredictionIO | 21 heures | PredictionIO is an open source Machine Learning Server built on top of state-of-the-art open source stack. Audience This course is directed at developers and data scientists who want to create predictive engines for any machine learning task. |
| bigdarch | Big Data Architect | 35 heures | Day 1 - provides a high-level overview of essential Big Data topic areas. The module is divided into a series of sections, each of which is accompanied by a hands-on exercise. Day 2 - explores a range of topics that relate analysis practices and tools for Big Data environments. It does not get into implementation or programming details, but instead keeps coverage at a conceptual level, focusing on topics that enable participants to develop a comprehensive understanding of the common analysis functions and features offered by Big Data solutions. Day 3 - provides an overview of the fundamental and essential topic areas relating to Big Data solution platform architecture. It covers Big Data mechanisms required for the development of a Big Data solution platform and architectural options for assembling a data processing platform. Common scenarios are also presented to provide a basic understanding of how a Big Data solution platform is generally used. Day 4 - builds upon Day 3 by exploring advanced topics relatng to Big Data solution platform architecture. In particular, different architectural layers that make up the Big Data solution platform are introduced and discussed, including data sources, data ingress, data storage, data processing and security. Day 5 - covers a number of exercises and problems designed to test the delegates ability to apply knowledge of topics covered Day 3 and 4. |
| scylladb | Scylla database | 21 heures | Scylla is an open-source distributed NoSQL data store. It is compatible with Apache Cassandra but performs at significantly higher throughputs and lower latencies. In this course, participants will learn about Scylla's features and architecture while obtaining practical experience with setting up, administering, monitoring, and troubleshooting Scylla. Audience Database administrators Developers System Engineers Format of the course The course is interactive and includes discussions of the principles and approaches for deploying and managing Scylla distributed databases and clusters. The course includes a heavy component of hands-on exercises and practice. |
| opennmt | OpenNMT: Setting up a Neural Machine Translation System | 7 heures | OpenNMT is a full-featured, open-source (MIT) neural machine translation system that utilizes the Torch mathematical toolkit. In this training participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor. By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution. Source and target language samples will be pre-arranged per the audience's requirements. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course Part lecture, part discussion, heavy hands-on practice |
| MicrosoftCognitiveToolkit | Microsoft Cognitive Toolkit 2.x | 21 heures | Microsoft Cognitive Toolkit 2.x (previously CNTK) is an open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. According to Microsoft, CNTK can be 5-10x faster than TensorFlow on recurrent networks, and 2 to 3 times faster than TensorFlow for image-related tasks. In this instructor-led, live training, participants will learn how to use Microsoft Cognitive Toolkit to create, train and evaluate deep learning algorithms for use in commercial-grade AI applications involving multiple types of data such as data, speech, text, and images. By the end of this training, participants will be able to: Access CNTK as a library from within a Python, C#, or C++ program Use CNTK as a standalone machine learning tool through its own model description language (BrainScript) Use the CNTK model evaluation functionality from a Java program Combine feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs) Scale computation capacity on CPUs, GPUs and multiple machines Access massive datasets using existing programming languages and algorithms Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note If you wish to customize any part of this training, including the programming language of choice, please contact us to arrange. |
| datavault | Data Vault: Building a Scalable Data Warehouse | 28 heures | Data vault modeling is a database modeling technique that provides long-term historical storage of data that originates from multiple sources. A data vault stores a single version of the facts, or "all the data, all of the time". Its flexible, scalable, consistent and adaptable design encompasses the best aspects of 3rd normal form (3NF) and star schema. In this instructor-led, live training, participants will learn how to build a Data Vault. By the end of this training, participants will be able to: Understand the architecture and design concepts behind Data Vault 2.0, and its interaction with Big Data, NoSQL and AI. Use data vaulting techniques to enable auditing, tracing, and inspection of historical data in a data warehouse Develop a consistent and repeatable ETL (Extract, Transform, Load) process Build and deploy highly scalable and repeatable warehouses Audience Data modelers Data warehousing specialist Business Intelligence specialists Data engineers Database administrators Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| nlg | Python for Natural Language Generation | 21 heures | Natural language generation (NLG) refers to the production of natural language text or speech by a computer. In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content. By the end of this training, participants will be able to: Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting Select and organize source content, plan sentences, and prepare a system for automatic generation of original content Understand the NLG pipeline and apply the right techniques at each stage Understand the architecture of a Natural Language Generation (NLG) system Implement the most suitable algorithms and models for analysis and ordering Pull data from publicly available data sources as well as curated databases to use as material for generated text Replace manual and laborious writing processes with computer-generated, automated content creation Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| python_nlp | Natural Language Processing with Deep Dive in Python and NLTK | 35 heures | By the end of the training the delegates are expected to be sufficiently equipped with the essential python concepts and should be able to sufficiently use NLTK to implement most of the NLP and ML based operations. The training is aimed at giving not just an executional knowledge but also the logical and operational knowledge of the technology therein. |
| dlforbankingwithr | Deep Learning for Banking (with R) | 28 heures | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in banking Use R to create deep learning models for banking Build their own deep learning credit risk model using R Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| chatbotpython | Building Chatbots in Python | 21 heures | ChatBots are computer programs that automatically simulate human responses via chat interfaces. ChatBots help organizations maximize their operations efficiency by providing easier and faster options for their user interactions. In this instructor-led, live training, participants will learn how to build chatbots in Python. By the end of this training, participants will be able to: Understand the fundamentals of building chatbots Build, test, deploy, and troubleshoot various chatbots using Python Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note To request a customized training for this course, please contact us to arrange. |
| sspsspas | Statistics with SPSS Predictive Analytics Software | 14 heures | Goal: Learning to work with SPSS at the level of independence The addressees: Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and learn popular data mining techniques. |
| 68780 | Apache Spark | 14 heures | |
| matlab2 | MATLAB Fundamentals | 21 heures | This three-day course provides a comprehensive introduction to the MATLAB technical computing environment. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include: Working with the MATLAB user interface Entering commands and creating variables Analyzing vectors and matrices Visualizing vector and matrix data Working with data files Working with data types Automating commands with scripts Writing programs with logic and flow control Writing functions |
| python_nltk | Traitement Automatique du Langage Naturel avec Python | 28 heures | This course introduces linguists or programmers to NLP in Python. During this course we will mostly use nltk.org (Natural Language Tool Kit), but also we will use other libraries relevant and useful for NLP. At the moment we can conduct this course in Python 2.x or Python 3.x. Examples are in English or Mandarin (普通话). Other languages can be also made available if agreed before booking. |
| cntk | Using Computer Network ToolKit (CNTK) | 28 heures | Computer Network ToolKit (CNTK) is Microsoft's Open Source, Multi-machine, Multi-GPU, Highly efficent RNN training machine learning framework for speech, text, and images. Audience This course is directed at engineers and architects aiming to utilize CNTK in their projects. |
| dlv | Deep Learning for Vision | 21 heures | Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source ) for analyzing computer images This course provide working examples. |
| aitech | Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP | 21 heures | This course is aimed at developers and data scientists who wish to understand and implement AI within their applications. Special focus is given to Data Analysis, Distributed AI and NLP. |
| intrdplrngrsneuing | Introduction Deep Learning & Réseaux de neurones pour l’ingénieur | 21 heures | Type : Formation théorique avec applications décidées en amont avec les élèves sur Lasagne ou Keras selon le groupe pédagogique Méthode pédagogique : présentation, échanges et études de cas L’intelligence artificielle, après avoir bouleversé de nombreux domaines scientifiques, a commencé à révolutionner un grand nombre de secteurs économiques (industrie, médecine, communication, etc.). Néanmoins, sa présentation dans les grands media relève souvent du fantasme, très éloignée de ce que sont réellement les domaines du Machine Learning ou du Deep Learning. L’objet de cette formation est d’apporter à des ingénieurs ayant déjà une maîtrise des outils informatiques (dont une base de programmation logicielle) une introduction au Deep Learning ainsi qu’à ses différents domaines de spécialisation et donc aux principales architectures de réseau existant aujourd’hui. Si les bases mathématiques sont rappelées pendant le cours, un niveau de mathématique de type BAC+2 est recommandé pour plus de confort. Il est dans l’absolu possible de faire l’impasse sur l’axe mathématique pour ne conserver qu’une vision « système », mais cette approche limitera énormément votre compréhension du sujet. |
| PentahoDI | Pentaho Data Integration Fundamentals | 21 heures | Pentaho Data Integration is an open-source data integration tool for defining jobs and data transformations. In this instructor-led, live training, participants will learn how to use Pentaho Data Integration's powerful ETL capabilities and rich GUI to manage an entire big data lifecycle, maximizing the value of data to the organization. By the end of this training, participants will be able to: Create, preview, and run basic data transformations containing steps and hops Configure and secure the Pentaho Enterprise Repository Harness disparate sources of data and generate a single, unified version of the truth in an analytics-ready format. Provide results to third-part applications for further processing Audience Data Analyst ETL developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| t2t | T2T: Creating Sequence to Sequence models for generalized learning | 7 heures | Tensor2Tensor (T2T) is a modular, extensible library for training AI models in different tasks, using different types of training data, for example: image recognition, translation, parsing, image captioning, and speech recognition. It is maintained by the Google Brain team. In this instructor-led, live training, participants will learn how to prepare a deep-learning model to resolve multiple tasks. By the end of this training, participants will be able to: Install tensor2tensor, select a data set, and train and evaluate an AI model Customize a development environment using the tools and components included in Tensor2Tensor Create and use a single model to concurrently learn a number of tasks from multiple domains Use the model to learn from tasks with a large amount of training data and apply that knowledge to tasks where data is limited Obtain satisfactory processing results using a single GPU Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| odmblockchain | IBM ODM and Blockchain: Applying business rules to Smart Contracts | 14 heures | Smart Contracts are used to encode and encapsulate the rules for automatically initiating and processing transactions on the Blockchain. In this instructor-led, live training, participants will learn how to use IBM Operational Decision Manager (ODM) with Hyperledger Composer to implement the business logic of a Smart Contract using business rules. By the end of this training, participants will be able to: Use ODM's rule engine together with Blockchain to "unbury" rules from the codebase of a Blockchain application Set up a system to allow specialist such as accountants, auditors, lawyers, and analysts to define the rules of exchange for themselves Use Decision Center as a platform to collaboratively govern rules Use ODM's rule engine to update, test and deploy rules without touching the code of the Smart Contract Deploy the IBM ODM Rule Execution Server Integrate IBM ODM with Hyperledger Composer running on Hyperledger Fabric Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| graphcomputing | Introduction to Graph Computing | 28 heures | A large number of real world problems can be described in terms of graphs. For example, the Web graph, the social network graph, the train network graph and the language graph. These graphs tend to be extremely large; processing them requires a specialized set of tools and processes -- these tools and processes can be referred to as Graph Computing (also known as Graph Analytics). In this instructor-led, live training, participants will learn about the technology offerings and implementation approaches for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using a graph computing approach. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments. By the end of this training, participants will be able to: Understand how graph data is persisted and traversed Select the best framework for a given task (from graph databases to batch processing frameworks) Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel View real-world big data problems in terms of graphs, processes and traversals Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| dlforfinancewithpython | Deep Learning for Finance (with Python) | 28 heures | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in finance Use Python, Keras, and TensorFlow to create deep learning models for finance Build their own deep learning stock price prediction model using Python Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| beam | Unified Batch and Stream Processing with Apache Beam | 14 heures | Apache Beam is an open source, unified programming model for defining and executing parallel data processing pipelines. It's power lies in its ability to run both batch and streaming pipelines, with execution being carried out by one of Beam’s supported distributed processing back-ends: Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Apache Beam is useful for ETL (Extract, Transform, and Load) tasks such as moving data between different storage media and data sources, transforming data into a more desirable format, and loading data onto a new system. In this instructor-led, live training (onsite or remote), participants will learn how to implement the Apache Beam SDKs in a Java or Python application that defines a data processing pipeline for decomposing a big data set into smaller chunks for independent, parallel processing. By the end of this training, participants will be able to: Install and configure Apache Beam Use a single programming model to carry out both batch and stream processing from withing their Java or Python application Execute pipelines across multiple environments Audience Developers Format of the Course Part lecture, part discussion, exercises and heavy hands-on practice Note This course will be available Scala in the future. Please contact us to arrange. |
| hadoopmapr | Administration de Hadoop avec MapR | 28 heures | Audience: This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand. |
| mlrobot1 | Machine Learning pour la Robotique | 21 heures | This course introduces machine learning methods in robotics applications. It is a broad overview of existing methods, motivations and main ideas in the context of pattern recognition. After a short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software. |
| mlfsas | Les Fondamentaux du Machine Learning avec Scala et Apache Spark | 14 heures | The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications. |
| systemml | Apache SystemML for Machine Learning | 14 heures | Apache SystemML is a distributed and declarative machine learning platform. SystemML provides declarative large-scale machine learning (ML) that aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations, to distributed computations on Apache Hadoop and Apache Spark. Audience This course is suitable for Machine Learning researchers, developers and engineers seeking to utilize SystemML as a framework for machine learning. |
| aiauto | Intelligence Artificielle pour l'Automobile | 14 heures | This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making. |
| marvin | Marvin Image Processing Framework - Creating Image and Video Processing Applications with Marvin | 14 heures | Marvin is an extensible, cross-platform, open-source image and video processing framework developed in Java. Developers can use Marvin to manipulate images, extract features from images for classification tasks, generate figures algorithmically, process video file datasets, and set up unit test automation. Some of Marvin's video applications include filtering, augmented reality, object tracking and motion detection. In this course participants will learn the principles of image and video analysis and utilize the Marvin Framework and its image processing algorithms to construct their own application. Audience Software developers wishing to utilize a rich, plug-in based open-source framework to create image and video processing applications Format of the course The basic principles of image analysis, video analysis and the Marvin Framework are first introduced. Students are given project-based tasks which allow them to practice the concepts learned. By the end of the class, participants will have developed their own application using the Marvin Framework and libraries. |
| mchdeeplearn | Introduction à l’intelligence artificielle : outils et enjeux | 14 heures | L’intelligence artificielle, après avoir bouleversé de nombreux domaines scientifiques, a commencé à révolutionner un grand nombre de secteurs économiques (industrie, médecine, communication, etc.). Néanmoins, sa présentation dans les grands media relève souvent du fantasme, très éloignée de ce que sont réellement les domaines du Machine Learning ou du Deep Learning. L’objet de cette formation est de présenter réellement ces approches et ce qu’elles apportent dans la résolution de problèmes considérés comme « intelligents ». Un grand nombre d’applications sont présentées, du traitement de donnée brute à la création de contenus « originaux » en passant par le contrôle d’agents, la classification automatisée ou l’approximation d’une donnée pour faciliter sa compréhension et sa manipulation. Enfin, un axe important est celui de l’opportunité comme de la méthodologie de mise en œuvre de tels projets. Le Deep Learning connaît comme tout outil de nombreuses limites, et son application suppose une réelle méthode pour comprendre, contrôler et garantir un résultat final de qualité. |
| hdp | Hortonworks Data Platform (HDP) for administrators | 21 heures | Hortonworks Data Platform is an open-source Apache Hadoop support platform that provides a stable foundation for developing big data solutions on the Apache Hadoop ecosystem. This instructor-led live training introduces Hortonworks and walks participants through the deployment of Spark + Hadoop solution. By the end of this training, participants will be able to: Use Hortonworks to reliably run Hadoop at a large scale Unify Hadoop's security, governance, and operations capabilities with Spark's agile analytic workflows. Use Hortonworks to investigate, validate, certify and support each of the components in a Spark project Process different types of data, including structured, unstructured, in-motion, and at-rest. Audience Hadoop administrators Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| embeddingprojector | Embedding Projector: Visualizing your Training Data | 14 heures | Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow. This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project. By the end of this training, participants will be able to: Explore how data is being interpreted by machine learning models Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals. Explore the properties of a specific embedding to understand the behavior of a model Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| fsharpfordatascience | F# for Data Science | 21 heures | Data science is the application of statistical analysis, machine learning, data visualization and programming for the purpose of understanding and interpreting real-world data. F# is a well suited programming language for data science as it combines efficient execution, REPL-scripting, powerful libraries and scalable data integration. In this instructor-led, live training, participants will learn how to use F# to solve a series of real-world data science problems. By the end of this training, participants will be able to: Use F#'s integrated data science packages Use F# to interoperate with other languages and platforms, including Excel, R, Matlab, and Python Use the Deedle package to solve time series problems Carry out advanced analysis with minimal lines of production-quality code Understand how functional programming is a natural fit for scientific and big data computations Access and visualize data with F# Apply F# for machine learning Explore solutions for problems in domains such as business intelligence and social gaming Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| pythonfinance | Python Programming for Finance | 35 heures | Python is a programming language that has gained huge popularity in the financial industry. Adopted by the largest investment banks and hedge funds, it is being used to build a wide range of financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to use Python to develop practical applications for solving a number of specific finance related problems. By the end of this training, participants will be able to: Understand the fundamentals of the Python programming language Download, install and maintain the best development tools for creating financial applications in Python Select and utilize the most suitable Python packages and programming techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.) Build applications that solve problems related to asset allocation, risk analysis, investment performance and more Troubleshoot, integrate, deploy, and optimize a Python application Audience Developers Analysts Quants Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange. |
| monetdb | MonetDB | 28 heures | MonetDB is an open-source database that pioneered the column-store technology approach. In this instructor-led, live training, participants will learn how to use MonetDB and how to get the most value out of it. By the end of this training, participants will be able to: Understand MonetDB and its features Install and get started with MonetDB Explore and perform different functions and tasks in MonetDB Accelerate the delivery of their project by maximizing MonetDB capabilities Audience Developers Technical experts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| datascienceintro | A Practical Introduction to Data Science | 35 heures | Participants who complete this training will gain a practical, real-world understanding of Data Science and its related technologies, methodologies and tools. Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class. The course starts with an introduction to elemental concepts of Data Science, then progresses into the tools and methodologies used in Data Science. Audience Developers Technical analysts IT consultants Format of the Course Part lecture, part discussion, exercises and heavy hands-on practice Note To request a customized training for this course, please contact us to arrange. |
| nlp | Natural Language Processing | 21 heures | This course has been designed for people interested in extracting meaning from written English text, though the knowledge can be applied to other human languages as well. The course will cover how to make use of text written by humans, such as blog posts, tweets, etc... For example, an analyst can set up an algorithm which will reach a conclusion automatically based on extensive data source. |
| MLFWR1 | Machine Learning Fundamentals with R | 14 heures | The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications. |
| deeplearning1 | Introduction au Deep Learning | 21 heures | This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction. |
| altdomexp | Analytics Domain Expertise | 7 heures | This course is part of the Data Scientist skill set (Domain: Analytics Domain Expertise). |
| jenetics | Jenetics | 21 heures | Jenetics is an advanced Genetic Algorithm, respectively an Evolutionary Algorithm, library written in modern day Java. Audience This course is directed at Researchers seeking to utilize Jenetics in their projects |
| Neuralnettf | Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple | 28 heures | This course will give you knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications). This training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow. |
| Torch | Torch: Getting started with Machine and Deep Learning | 21 heures | Torch is an open source machine learning library and a scientific computing framework based on the Lua programming language. It provides a development environment for numerics, machine learning, and computer vision, with a particular emphasis on deep learning and convolutional nets. It is one of the fastest and most flexible frameworks for Machine and Deep Learning and is used by companies such as Facebook, Google, Twitter, NVIDIA, AMD, Intel, and many others. In this course we cover the principles of Torch, its unique features, and how it can be applied in real-world applications. We step through numerous hands-on exercises all throughout, demonstrating and practicing the concepts learned. By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects. Audience Software developers and programmers wishing to enable Machine and Deep Learning within their applications Format of the course Overview of Machine and Deep Learning In-class coding and integration exercises Test questions sprinkled along the way to check understanding |
| Fairsec | Fairsec: Setting up a CNN-based machine translation system | 7 heures | Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT). In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Source and target language content samples can be prepared according to audience's requirements. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course Part lecture, part discussion, heavy hands-on practice |
| magellan | Magellan: Geospatial Analytics on Spark | 14 heures | Magellan is an open-source distributed execution engine for geospatial analytics on big data. Implemented on top of Apache Spark, it extends Spark SQL and provides a relational abstraction for geospatial analytics. This instructor-led, live training introduces the concepts and approaches for implementing geospacial analytics and walks participants through the creation of a predictive analysis application using Magellan on Spark. By the end of this training, participants will be able to: Efficiently query, parse and join geospatial datasets at scale Implement geospatial data in business intelligence and predictive analytics applications Use spatial context to extend the capabilities of mobile devices, sensors, logs, and wearables Audience Application developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| openface | OpenFace: Creating Facial Recognition Systems | 14 heures | OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google’s FaceNet research. In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application. By the end of this training, participants will be able to: Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation. Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc. Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| mlios | Machine Learning on iOS | 14 heures | In this instructor-led, live training, participants will learn how to use the iOS Machine Learning (ML) technology stack as they as they step through the creation and deployment of an iOS mobile app. By the end of this training, participants will be able to: Create a mobile app capable of image processing, text analysis and speech recognition Access pre-trained ML models for integration into iOS apps Create a custom ML model Add Siri Voice support to iOS apps Understand and use frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit Use languages and tools such as Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| rforfinance | R Programming for Finance | 28 heures | R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to use R to develop practical applications for solving a number of specific finance related problems. By the end of this training, participants will be able to: Understand the fundamentals of the R programming language Select and utilize R packages and techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.) Build applications that solve problems related to asset allocation, risk analysis, investment performance and more Troubleshoot, integrate deploy and optimize an R application Audience Developers Analysts Quants Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange. |
| apachedrill | Apache Drill for On-the-Fly Analysis of Multiple Big Data Formats | 21 heures | Apache Drill is a schema-free, distributed, in-memory columnar SQL query engine for Hadoop, NoSQL and other Cloud and file storage systems. The power of Apache Drill lies in its ability to join data from multiple data stores using a single query. Apache Drill supports numerous NoSQL databases and file systems, including HBase, MongoDB, MapR-DB, HDFS, MapR-FS, Amazon S3, Azure Blob Storage, Google Cloud Storage, Swift, NAS and local files. Apache Drill is the open source version of Google's Dremel system which is available as an infrastructure service called Google BigQuery. In this instructor-led, live training, participants will learn the fundamentals of Apache Drill, then leverage the power and convenience of SQL to interactively query big data across multiple data sources, without writing code. Participants will also learn how to optimize their Drill queries for distributed SQL execution. By the end of this training, participants will be able to: Perform "self-service" exploration on structured and semi-structured data on Hadoop Query known as well as unknown data using SQL queries Understand how Apache Drills receives and executes queries Write SQL queries to analyze different types of data, including structured data in Hive, semi-structured data in HBase or MapR-DB tables, and data saved in files such as Parquet and JSON. Use Apache Drill to perform on-the-fly schema discovery, bypassing the need for complex ETL and schema operations Integrate Apache Drill with BI (Business Intelligence) tools such as Tableau, Qlikview, MicroStrategy and Excel Audience Data analysts Data scientists SQL programmers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| arrow | Apache Arrow for Data Analysis across Disparate Data Sources | 14 heures | Apache Arrow is an open-source in-memory data processing framework. It is often used together with other data science tools for accessing disparate data stores for analysis. It integrates well with other technologies such as GPU databases, machine learning libraries and tools, execution engines, and data visualization frameworks. In this onsite instructor-led, live training, participants will learn integrate Apache Arrow with various Data Science frameworks to access data from disparate data sources. By the end of this training, participants will be able to: Install and configure Apache Arrow in a distributed clustered environment Use Apache Arrow to access data from disparate data sources Use Apache Arrow to bypass the need for constructing and maintaining complex ETL pipelines Analyze data across disparate data sources without having to consolidate it into a centralized repository Audience Data scientists Data engineers Format of the Course Part lecture, part discussion, exercises and heavy hands-on practice Note To request a customized training for this course, please contact us to arrange. |
| wfsadm | WildFly Server Administration | 14 heures | This course is created for Administrators, Developers or anyone who is interested in managing WildFly Application Server (AKA JBoss Application Server). This course usually runs on the newest version of the Application Server, but it can be tailored (as a private course) to older versions starting from version 5.1. |
| mlfunpython | Machine Learning avec Python, les Fondamentaux | 14 heures | The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications. |
| matlabml1 | Introduction au Machine Learning avec MATLAB | 21 heures | MATLAB is a numerical computing environment and programming language developed by MathWorks. |
| predmodr | Predictive Modelling with R | 14 heures | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining. |
| simplecv | Computer Vision with SimpleCV | 14 heures | SimpleCV is an open source framework — meaning that it is a collection of libraries and software that you can use to develop vision applications. It lets you work with the images or video streams that come from webcams, Kinects, FireWire and IP cameras, or mobile phones. It’s helps you build software to make your various technologies not only see the world, but understand it too. Audience This course is directed at engineers and developers seeking to develop computer vision applications with SimpleCV. |
| cassadmin | Administration de Cassandra | 14 heures | This course will introduce Cassandra – a popular NoSQL database. It will cover Cassandra principles, architecture and data model. Students will learn data modeling in CQL (Cassandra Query Language) in hands-on, interactive labs. This session also discusses Cassandra internals and some admin topics. |
| OpenNN | OpenNN: Mise en Oeuvre de Réseaux Neurone | 14 heures | OpenNN is an open-source class library written in C++ which implements neural networks, for use in machine learning. In this course we go over the principles of neural networks and use OpenNN to implement a sample application. Audience Software developers and programmers wishing to create Deep Learning applications. Format of the course Lecture and discussion coupled with hands-on exercises. |
| ApHadm1 | Apache Hadoop: Manipulation et Transformation des Données de Performance | 21 heures | This course is intended for developers, architects, data scientists or any profile that requires access to data either intensively or on a regular basis. The major focus of the course is data manipulation and transformation. Among the tools in the Hadoop ecosystem this course includes the use of Pig and Hive both of which are heavily used for data transformation and manipulation. This training also addresses performance metrics and performance optimisation. The course is entirely hands on and is punctuated by presentations of the theoretical aspects. |
| zeppelin | Zeppelin for interactive data analytics | 14 heures | Apache Zeppelin is a web-based notebook for capturing, exploring, visualizing and sharing Hadoop and Spark based data. This instructor-led, live training introduces the concepts behind interactive data analytics and walks participants through the deployment and usage of Zeppelin in a single-user or multi-user environment. By the end of this training, participants will be able to: Install and configure Zeppelin Develop, organize, execute and share data in a browser-based interface Visualize results without referring to the command line or cluster details Execute and collaborate on long workflows Work with any of a number of plug-in language/data-processing-backends, such as Scala ( with Apache Spark ), Python ( with Apache Spark ), Spark SQL, JDBC, Markdown and Shell. Integrate Zeppelin with Spark, Flink and Map Reduce Secure multi-user instances of Zeppelin with Apache Shiro Audience Data engineers Data analysts Data scientists Software developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| fiji | Fiji: Introduction to scientific image processing | 21 heures | Fiji is an open-source image processing package that bundles ImageJ (an image processing program for scientific multidimensional images) and a number of plugins for scientific image analysis. In this instructor-led, live training, participants will learn how to use the Fiji distribution and its underlying ImageJ program to create an image analysis application. By the end of this training, participants will be able to: Use Fiji's advanced programming features and software components to extend ImageJ Stitch large 3d images from overlapping tiles Automatically update a Fiji installation on startup using the integrated update system Select from a broad selection of scripting languages to build custom image analysis solutions Use Fiji's powerful libraries, such as ImgLib on large bioimage datasets Deploy their application and collaborate with other scientists on similar projects Audience Scientists Researchers Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| tidyverse | Introduction to Data Visualization with Tidyverse and R | 7 heures | The Tidyverse is a collection of versatile R packages for cleaning, processing, modeling, and visualizing data. Some of the packages included are: ggplot2, dplyr, tidyr, readr, purrr, and tibble. In this instructor-led, live training, participants will learn how to manipulate and visualize data using the tools included in the Tidyverse. By the end of this training, participants will be able to: Perform data analysis and create appealing visualizations Draw useful conclusions from various datasets of sample data Filter, sort and summarize data to answer exploratory questions Turn processed data into informative line plots, bar plots, histograms Import and filter data from diverse data sources, including Excel, CSV, and SPSS files Audience Beginners to the R language Beginners to data analysis and data visualization Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| textsum | Text Summarization with Python | 14 heures | In Python Machine Learning, the Text Summarization feature is able to read the input text and produce a text summary. This capability is available from the command-line or as a Python API/Library. One exciting application is the rapid creation of executive summaries; this is particularly useful for organizations that need to review large bodies of text data before generating reports and presentations. In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text. By the end of this training, participants will be able to: Use a command-line tool that summarizes text. Design and create Text Summarization code using Python libraries. Evaluate three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17 Audience Developers Data Scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| cortana | Turning Data into Intelligent Action with Cortana Intelligence | 28 heures | Cortana Intelligence Suite is a bundle of integrated products and services on the Microsoft Azure Cloud that enable entities to transform data into intelligent actions. In this instructor-led, live training, participants will learn how to use the components that are part of the Cortana Intelligence Suite to build data-driven intelligent applications. By the end of this training, participants will be able to: Learn how to use Cortana Intelligence Suite tools Acquire the latest knowledge of data management and analytics Use Cortana components to turn data into intelligent action Use Cortana to build applications from scratch and launch it on the cloud Audience Data scientists Programmers Developers Managers Architects Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| bigdataanahealth | Big Data Analytics in Health | 21 heures | Big data analytics involves the process of examining large amounts of varied data sets in order to uncover correlations, hidden patterns, and other useful insights. The health industry has massive amounts of complex heterogeneous medical and clinical data. Applying big data analytics on health data presents huge potential in deriving insights for improving delivery of healthcare. However, the enormity of these datasets poses great challenges in analyses and practical applications to a clinical environment. In this instructor-led, live training (remote), participants will learn how to perform big data analytics in health as they step through a series of hands-on live-lab exercises. By the end of this training, participants will be able to: Install and configure big data analytics tools such as Hadoop MapReduce and Spark Understand the characteristics of medical data Apply big data techniques to deal with medical data Study big data systems and algorithms in the context of health applications Audience Developers Data Scientists Format of the Course Part lecture, part discussion, exercises and heavy hands-on practice. Note To request a customized training for this course, please contact us to arrange. |
| mdlmrah | Model MapReduce and Apache Hadoop | 14 heures | The course is intended for IT specialist that works with the distributed processing of large data sets across clusters of computers. |
| mdldromgdmn | Modelling Decision and Rules with OMG DMN | 14 heures | This course teaches how to design and execute decisions in rules with OMG DMN (Decision Model and Notation) standard. |
| dladv | Deep Learning, Niveau Avancé | 28 heures | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. |
| dmmlr | Data Mining & Machine Learning avec R | 14 heures | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining. |
| spmllib | Apache Spark MLlib | 35 heures | MLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs. It divides into two packages: spark.mllib contains the original API built on top of RDDs. spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines. Audience This course is directed at engineers and developers seeking to utilize a built in Machine Library for Apache Spark |
| bpmndmncmmn | BPMN, DMN, and CMNN - OMG standards for process improvement | 28 heures | Business Process Model and Notation (BPMN), Decision Model and Notation (DMN) and Case Management Model and Notation (CMMN) are three Object Management Group (OMG) standards for processes, decisions, and case modelling. This course provides an introduction to all of them and informs when should we use which. |
| drools7int | Introduction to Drools 7 for Developers | 21 heures | This 3 days course is aimed to introduce Drools 7 to developers.This course doesn't cover drools integration, performance or any other complex topics. |
| hadoopforprojectmgrs | Hadoop for Project Managers | 14 heures | As more and more software and IT projects migrate from local processing and data management to distributed processing and big data storage, Project Managers are finding the need to upgrade their knowledge and skills to grasp the concepts and practices relevant to Big Data projects and opportunities. This course introduces Project Managers to the most popular Big Data processing framework: Hadoop. In this instructor-led training, participants will learn the core components of the Hadoop ecosystem and how these technologies can be used to solve large-scale problems. In learning these foundations, participants will also improve their ability to communicate with the developers and implementers of these systems as well as the data scientists and analysts that many IT projects involve. Audience Project Managers wishing to implement Hadoop into their existing development or IT infrastructure Project Managers needing to communicate with cross-functional teams that include big data engineers, data scientists and business analysts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| samza | Samza for Stream Processing | 14 heures | Apache Samza is an open-source near-realtime, asynchronous computational framework for stream processing. It uses Apache Kafka for messaging, and Apache Hadoop YARN for fault tolerance, processor isolation, security, and resource management. This instructor-led, live training introduces the principles behind messaging systems and distributed stream processing, while walking participants through the creation of a sample Samza-based project and job execution. By the end of this training, participants will be able to: Use Samza to simplify the code needed to produce and consume messages Decouple the handling of messages from an application Use Samza to implement near-realtime asynchronous computation Use stream processing to provide a higher level of abstraction over messaging systems Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| kdbplusandq | kdb+ and q: Analyze time series data | 21 heures | kdb+ is an in-memory, column-oriented database and q is its built-in, interpreted vector-based language. In kdb+, tables are columns of vectors and q is used to perform operations on the table data as if it was a list. kdb+ and q are commonly used in high frequency trading and are popular with the major financial institutions, including Goldman Sachs, Morgan Stanley, Merrill Lynch, JP Morgan, etc. In this instructor-led, live training, participants will learn how to create a time series data application using kdb+ and q. By the end of this training, participants will be able to: Understand the difference between a row-oriented database and a column-oriented database Select data, write scripts and create functions to carry out advanced analytics Analyze time series data such as stock and commodity exchange data Use kdb+'s in-memory capabilities to store, analyze, process and retrieve large data sets at high speed Think of functions and data at a higher level than the standard function(arguments) approach common in non-vector languages Explore other time-sensitive applications for kdb+, including energy trading, telecommunications, sensor data, log data, and machine and network usage monitoring Audience Developers Database engineers Data scientists Data analysts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| nifi | Apache NiFi for Administrators | 21 heures | Apache NiFi (Hortonworks DataFlow) is a real-time integrated data logistics and simple event processing platform that enables the moving, tracking and automation of data between systems. It is written using flow-based programming and provides a web-based user interface to manage dataflows in real time. In this instructor-led, live training, participants will learn how to deploy and manage Apache NiFi in a live lab environment. By the end of this training, participants will be able to: Install and configure Apachi NiFi Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes Automate dataflows Enable streaming analytics Apply various approaches for data ingestion Transform Big Data and into business insights Audience System administrators Data engineers Developers DevOps Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | 28 heures | Deep Learning for NLP allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP. In this instructor-led, live training, participants will learn to use Python libraries for NLP (Natural Language Processing) as they create an application that processes a set of pictures and generates captions. By the end of this training, participants will be able to: Design and code DL for NLP using Python libraries Create Python code that reads a substantially huge collection of pictures and generates keywords Create Python Code that generates captions from the detected keywords Audience Programmers with interest in linguistics Programmers who seek an understanding of NLP (Natural Language Processing) Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| intelligentmobileapps | Building Intelligent Mobile Applications | 35 heures | Intelligent applications are next generation apps that can continually learn from user interactions to provide better value and relevance to users. In this instructor-led, live training, participants will learn how to build intelligent mobile applications and bots. By the end of this training, participants will be able to: Understand the fundamental concepts of intelligent applications Learn how to use various tools for building intelligent applications Build intelligent applications using Azure, Cognitive Services API, Stream Analytics, and Xamarin Audience Developers Programmers Hobbyists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| qgis | QGIS for Geographic Information System | 21 heures | A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data. The acronym GIS is sometimes used for geographic information science (GIScience) to refer to the academic discipline that studies geographic information systems and is a large domain within the broader academic discipline of geoinformatics. QGIS functions as geographic information system (GIS) software, allowing users to analyze and edit spatial information, in addition to composing and exporting graphical maps. QGIS supports both raster and vector layers; vector data is stored as either point, line, or polygon features. Multiple formats of raster images are supported, and the software can georeference images. To summarize it allows the users to Create, edit, visualise, analyse and publish geospatial information on Windows, Mac, Linux, BSD. This program, in its first phase, introduces the QGIS interface for general usage. In the second phase, we introduce PyQGIS - the python libraries of QGIS that allows the integration of GIS functionalities in your python code or your python application, so that you may even create your own Python Plugin around a particular GIS functionality. |
| rneuralnet | Neural Network avec R | 14 heures | This course is an introduction to applying neural networks in real world problems using R-project software. |
| 68736 | Hadoop for Developers (2 days) | 14 heures | |
| bigdatastore | Big Data Storage Solution - NoSQL | 14 heures | When traditional storage technologies don't handle the amount of data you need to store there are hundereds of alternatives. This course try to guide the participants what are alternatives for storing and analyzing Big Data and what are theirs pros and cons. This course is mostly focused on discussion and presentation of solutions, though hands-on exercises are available on demand. |
| rprogda | R Programming for Data Analysis | 14 heures | This course is part of the Data Scientist skill set (Domain: Data and Technology) |
| aiintrozero | From Zero to AI | 35 heures | This course is created for people who have no previous experience in probability and statistics. |
| mldt | Machine Learning et Deep Learning | 21 heures | This course covers AI (emphasizing Machine Learning and Deep Learning) |
| drools7dslba | Drools 7 and DSL for Business Analysts | 21 heures | This 3 days course is aimed to introduce Drools 7 to Business Analysts responsible for writing tests and rules. This course focuses on creating pure logic. Analysts after this course can writing tests and logic which then can be further integrated by developers with business applications. |
| Fairseq | Fairseq: Setting up a CNN-based machine translation system | 7 heures | Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT). In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course Part lecture, part discussion, heavy hands-on practice Note If you wish to use specific source and target language content, please contact us to arrange. |
| flink | Flink for Scalable Stream and Batch Data Processing | 28 heures | Apache Flink is an open-source framework for scalable stream and batch data processing. This instructor-led, live training introduces the principles and approaches behind distributed stream and batch data processing, and walks participants through the creation of a real-time, data streaming application. By the end of this training, participants will be able to: Set up an environment for developing data analysis applications Package, execute, and monitor Flink-based, fault-tolerant, data streaming applications Manage diverse workloads Perform advanced analytics using Flink ML Set up a multi-node Flink cluster Measure and optimize performance Integrate Flink with different Big Data systems Compare Flink capabilities with those of other big data processing frameworks Audience Developers Architects Data engineers Analytics professionals Technical managers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| zendfundamentals | Zend Framework: Fundamentals | 21 heures | Zend framework is an open-source, object-orientated framework for developing, deploying, and managing enterprise-ready PHP based web applications and services. Zend framework utilizes the Model-View-Controller (MVC) paradigm to develop basic structures for applications. Zend is considered a "component library"; its unique modular design enables users to use components independently of one another. In this instructor-led, live training, participants will learn how to create a reliable and scalable web application using the Zend framework. By the end of this training, participants will be able to: Use Model-View-Controller design patterns to build a database-based web application Receive and process forms Set up input validation and view scripts Handle the various types of MVC events and services offered by Zend Framework MVC component library Prepare and execute queries for a database adapter Audience Intermediate to advanced PHP developers seeking to develop secure, enterprise scale web-applications Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| nifidev | Apache NiFi for Developers | 7 heures | Apache NiFi (Hortonworks DataFlow) is a real-time integrated data logistics and simple event processing platform that enables the moving, tracking and automation of data between systems. It is written using flow-based programming and provides a web-based user interface to manage dataflows in real time. In this instructor-led, live training, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi. By the end of this training, participants will be able to: Understand NiFi's architecture and dataflow concepts Develop extensions using NiFi and third-party APIs Custom develop their own Apache Nifi processor Ingest and process real-time data from disparate and uncommon file formats and data sources Audience Developers Data engineers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| smartrobot | Smart Robots for Developers | 84 heures | A Smart Robot is an Artificial Intelligence (AI) system that can learn from its environment and its experience and build on its capabilities based on that knowledge. Smart Robots can collaborate with humans, working along-side them and learning from their behavior. Furthermore, they have the capacity for not only manual labor, but cognitive tasks as well. In addition to physical robots, Smart Robots can also be purely software based, residing in a computer as a software application with no moving parts or physical interaction with the world. In this instructor-led, live training, participants will learn the different technologies, frameworks and techniques for programming different types of mechanical Smart Robots, then apply this knowledge to complete their own Smart Robot projects. The course is divided into 4 sections, each consisting of three days of lectures, discussions, and hands-on robot development in a live lab environment. Each section will conclude with a practical hands-on project to allow participants to practice and demonstrate their acquired knowledge. The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots. By the end of this training, participants will be able to: Understand the key concepts used in robotic technologies Understand and manage the interaction between software and hardware in a robotic system Understand and implement the software components that underpin Smart Robots Build and operate a simulated mechanical Smart Robot that can see, sense, process, grasp, navigate, and interact with humans through voice Extend a Smart Robot's ability to perform complex tasks through Deep Learning Test and troubleshoot a Smart Robot in realistic scenarios Audience Developers Engineers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note To customize any part of this course (programming language, robot model, etc.) please contact us to arrange. |
| aifortelecom | AI Awareness for Telecom | 14 heures | AI is a collection of technologies for building intelligent systems capable of understanding data and the activities surrounding the data to make "intelligent decisions". For Telecom providers, building applications and services that make use of AI could open the door for improved operations and servicing in areas such as maintenance and network optimization. In this course we examine the various technologies that make up AI and the skill sets required to put them to use. Throughout the course, we examine AI's specific applications within the Telecom industry. Audience Network engineers Network operations personnel Telecom technical managers Format of the course Part lecture, part discussion, hands-on exercises |
| DM7 | Getting started with DM7 | 21 heures | Audience Beginner or intermediate database developers Beginner or intermediate database administrators Programmers Format of the course Heavy emphasis on hands-on practice. Most of the concepts are learned through samples, exercises and hands-on development |
| bdbitcsp | Big Data Business Intelligence for Telecom and Communication Service Providers | 35 heures | Overview Communications service providers (CSP) are facing pressure to reduce costs and maximize average revenue per user (ARPU), while ensuring an excellent customer experience, but data volumes keep growing. Global mobile data traffic will grow at a compound annual growth rate (CAGR) of 78 percent to 2016, reaching 10.8 exabytes per month. Meanwhile, CSPs are generating large volumes of data, including call detail records (CDR), network data and customer data. Companies that fully exploit this data gain a competitive edge. According to a recent survey by The Economist Intelligence Unit, companies that use data-directed decision-making enjoy a 5-6% boost in productivity. Yet 53% of companies leverage only half of their valuable data, and one-fourth of respondents noted that vast quantities of useful data go untapped. The data volumes are so high that manual analysis is impossible, and most legacy software systems can’t keep up, resulting in valuable data being discarded or ignored. With Big Data & Analytics’ high-speed, scalable big data software, CSPs can mine all their data for better decision making in less time. Different Big Data products and techniques provide an end-to-end software platform for collecting, preparing, analyzing and presenting insights from big data. Application areas include network performance monitoring, fraud detection, customer churn detection and credit risk analysis. Big Data & Analytics products scale to handle terabytes of data but implementation of such tools need new kind of cloud based database system like Hadoop or massive scale parallel computing processor ( KPU etc.) This course work on Big Data BI for Telco covers all the emerging new areas in which CSPs are investing for productivity gain and opening up new business revenue stream. The course will provide a complete 360 degree over view of Big Data BI in Telco so that decision makers and managers can have a very wide and comprehensive overview of possibilities of Big Data BI in Telco for productivity and revenue gain. Course objectives Main objective of the course is to introduce new Big Data business intelligence techniques in 4 sectors of Telecom Business (Marketing/Sales, Network Operation, Financial operation and Customer Relation Management). Students will be introduced to following: Introduction to Big Data-what is 4Vs (volume, velocity, variety and veracity) in Big Data- Generation, extraction and management from Telco perspective How Big Data analytic differs from legacy data analytic In-house justification of Big Data -Telco perspective Introduction to Hadoop Ecosystem- familiarity with all Hadoop tools like Hive, Pig, SPARC –when and how they are used to solve Big Data problem How Big Data is extracted to analyze for analytics tool-how Business Analysis’s can reduce their pain points of collection and analysis of data through integrated Hadoop dashboard approach Basic introduction of Insight analytics, visualization analytics and predictive analytics for Telco Customer Churn analytic and Big Data-how Big Data analytic can reduce customer churn and customer dissatisfaction in Telco-case studies Network failure and service failure analytics from Network meta-data and IPDR Financial analysis-fraud, wastage and ROI estimation from sales and operational data Customer acquisition problem-Target marketing, customer segmentation and cross-sale from sales data Introduction and summary of all Big Data analytic products and where they fit into Telco analytic space Conclusion-how to take step-by-step approach to introduce Big Data Business Intelligence in your organization Target Audience Network operation, Financial Managers, CRM managers and top IT managers in Telco CIO office. Business Analysts in Telco CFO office managers/analysts Operational managers QA managers |
| pmml | Predictive Models with PMML | 7 heures | The course is created to scientific, developers, analysts or any other people who want to standardize or exchange their models with Predictive Model Markup Language (PMML) file format. |
| hadoopdeva | Hadoop pour Développeurs, niveau Acvancé | 21 heures | Apache Hadoop is one of the most popular frameworks for processing Big Data on clusters of servers. This course delves into data management in HDFS, advanced Pig, Hive, and HBase. These advanced programming techniques will be beneficial to experienced Hadoop developers. Audience: developers Duration: three days Format: lectures (50%) and hands-on labs (50%). |
| bigddbsysfun | Big Data & Database Systems Fundamentals | 14 heures | The course is part of the Data Scientist skill set (Domain: Data and Technology). |
| caffe | Deep Learning pour la Vision avec Caffe | 21 heures | Caffe is a deep learning framework made with expression, speed, and modularity in mind. This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework. After completing this course, delegates will be able to: understand Caffe’s structure and deployment mechanisms carry out installation / production environment / architecture tasks and configuration assess code quality, perform debugging, monitoring implement advanced production like training models, implementing layers and logging |
| dsbda | Data Science for Big Data Analytics | 35 heures | Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy. |
| accumulo | Apache Accumulo: Building highly scalable big data applications | 21 heures | Apache Accumulo is a sorted, distributed key/value store that provides robust, scalable data storage and retrieval. It is based on the design of Google's BigTable and is powered by Apache Hadoop, Apache Zookeeper, and Apache Thrift. This courses covers the working principles behind Accumulo and walks participants through the development of a sample application on Apache Accumulo. Audience Application developers Software engineers Technical consultants Format of the course Part lecture, part discussion, hands-on development and implementation, occasional tests to gauge understanding |
| facebooknmt | Facebook NMT: Setting up a Neural Machine Translation System | 7 heures | Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT). In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course Part lecture, part discussion, heavy hands-on practice Note If you wish to use specific source and target language content, please contact us to arrange. |
| alluxio | Alluxio: Unifying disparate storage systems | 7 heures | Alexio is an open-source virtual distributed storage system that unifies disparate storage systems and enables applications to interact with data at memory speed. It is used by companies such as Intel, Baidu and Alibaba. In this instructor-led, live training, participants will learn how to use Alexio to bridge different computation frameworks with storage systems and efficiently manage multi-petabyte scale data as they step through the creation of an application with Alluxio. By the end of this training, participants will be able to: Develop an application with Alluxio Connect big data systems and applications while preserving one namespace Efficiently extract value from big data in any storage format Improve workload performance Deploy and manage Alluxio standalone or clustered Audience Data scientist Developer System administrator Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| solrcloud | SolrCloud | 14 heures | Apache SolrCloud is a distributed data processing engine that facilitates the searching and indexing of files on a distributed network. In this instructor-led, live training, participants will learn how to set up a SolrCloud instance on Amazon AWS. By the end of this training, participants will be able to: Understand SolCloud's features and how they compare to those of conventional master-slave clusters Configure a SolCloud centralized cluster Automate processes such as communicating with shards, adding documents to the shards, etc. Use Zookeeper in conjunction with SolrCloud to further automate processes Use the interface to manage error reporting Load balance a SolrCloud installation Configure SolrCloud for continuous processing and fail-over Audience Solr Developers Project Managers System Administrators Search Analysts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| matlabpredanalytics | Matlab for Predictive Analytics | 21 heures | Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events. In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data. By the end of this training, participants will be able to: Create predictive models to analyze patterns in historical and transactional data Use predictive modeling to identify risks and opportunities Build mathematical models that capture important trends Use data to from devices and business systems to reduce waste, save time, or cut costs Audience Developers Engineers Domain experts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| bigdatabicriminal | Big Data Business Intelligence for Criminal Intelligence Analysis | 35 heures | Advances in technologies and the increasing amount of information are transforming how law enforcement is conducted. The challenges that Big Data pose are nearly as daunting as Big Data's promise. Storing data efficiently is one of these challenges; effectively analyzing it is another. In this instructor-led, live training, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results. By the end of this training, participants will be able to: Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation Implement industrial big data storage and processing solutions for data analysis Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation Audience Law Enforcement specialists with a technical background Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| tableaupython | Tableau with Python | 14 heures | Tableau is a business intelligence and data visualization tool. Python is a widely used programming language which provides support for a wide variety of statistical and machine learning techniques. Tableau's data visualization power and Python's machine learning capabilities, when combined, help developers rapidly build advanced data analytics applications for various business use cases. In this instructor-led, live training, participants will learn how to combine Tableau and Python to carry out advanced analytics. Integration of Tableau and Python will be done via the TabPy API. By the end of this training, participants will be able to: Integrate Tableau and Python using TabPy API Use the integration of Tableau and Python to analyze complex business scenarios with few lines of Python code Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| bdbiga | Big Data Business Intelligence for Govt. Agencies | 35 heures | Advances in technologies and the increasing amount of information are transforming how business is conducted in many industries, including government. Government data generation and digital archiving rates are on the rise due to the rapid growth of mobile devices and applications, smart sensors and devices, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes more complex, information management, processing, storage, security, and disposition become more complex as well. New capture, search, discovery, and analysis tools are helping organizations gain insights from their unstructured data. The government market is at a tipping point, realizing that information is a strategic asset, and government needs to protect, leverage, and analyze both structured and unstructured information to better serve and meet mission requirements. As government leaders strive to evolve data-driven organizations to successfully accomplish mission, they are laying the groundwork to correlate dependencies across events, people, processes, and information. High-value government solutions will be created from a mashup of the most disruptive technologies: Mobile devices and applications Cloud services Social business technologies and networking Big Data and analytics IDC predicts that by 2020, the IT industry will reach $5 trillion, approximately $1.7 trillion larger than today, and that 80% of the industry's growth will be driven by these 3rd Platform technologies. In the long term, these technologies will be key tools for dealing with the complexity of increased digital information. Big Data is one of the intelligent industry solutions and allows government to make better decisions by taking action based on patterns revealed by analyzing large volumes of data — related and unrelated, structured and unstructured. But accomplishing these feats takes far more than simply accumulating massive quantities of data.“Making sense of thesevolumes of Big Datarequires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information,” Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote in a post on the OSTP Blog. The White House took a step toward helping agencies find these technologies when it established the National Big Data Research and Development Initiative in 2012. The initiative included more than $200 million to make the most of the explosion of Big Data and the tools needed to analyze it. The challenges that Big Data poses are nearly as daunting as its promise is encouraging. Storing data efficiently is one of these challenges. As always, budgets are tight, so agencies must minimize the per-megabyte price of storage and keep the data within easy access so that users can get it when they want it and how they need it. Backing up massive quantities of data heightens the challenge. Analyzing the data effectively is another major challenge. Many agencies employ commercial tools that enable them to sift through the mountains of data, spotting trends that can help them operate more efficiently. (A recent study by MeriTalk found that federal IT executives think Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.). Custom-developed Big Data tools also are allowing agencies to address the need to analyze their data. For example, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has helped medical researchers find a link that can alert doctors to aortic aneurysms before they strike. It’s also used for more mundane tasks, such as sifting through résumés to connect job candidates with hiring managers. |
| dataminr | Data Mining with R | 14 heures | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining. |
| hadoopdev | Hadoop for Developers (4 days) | 28 heures | Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. This course will introduce a developer to various components (HDFS, MapReduce, Pig, Hive and HBase) Hadoop ecosystem. |
| rintrob | Introductory R for Biologists | 28 heures | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining. |
| datavis1 | Data Visualization | 28 heures | This course is intended for engineers and decision makers working in data mining and knoweldge discovery. You will learn how to create effective plots and ways to present and represent your data in a way that will appeal to the decision makers and help them to understand hidden information. |
| zookeeper | Apache Zookeeper | 14 heures | ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. |
| druid | Druid: Build a fast, real-time data analysis system | 21 heures | Druid is an open-source, column-oriented, distributed data store written in Java. It was designed to quickly ingest massive quantities of event data and execute low-latency OLAP queries on that data. Druid is commonly used in business intelligence applications to analyze high volumes of real-time and historical data. It is also well suited for powering fast, interactive, analytic dashboards for end-users. Druid is used by companies such as Alibaba, Airbnb, Cisco, eBay, Netflix, Paypal, and Yahoo. In this course we explore some of the limitations of data warehouse solutions and discuss how Druid can compliment those technologies to form a flexible and scalable streaming analytics stack. We walk through many examples, offering participants the chance to implement and test Druid-based solutions in a lab environment. Audience Application developers Software engineers Technical consultants DevOps professionals Architecture engineers Format of the course Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding |
| storm | Apache Storm | 28 heures | Apache Storm is a distributed, real-time computation engine used for enabling real-time business intelligence. It does so by enabling applications to reliably process unbounded streams of data (a.k.a. stream processing). "Storm is for real-time processing what Hadoop is for batch processing!" In this instructor-led live training, participants will learn how to install and configure Apache Storm, then develop and deploy an Apache Storm application for processing big data in real-time. Some of the topics included in this training include: Apache Storm in the context of Hadoop Working with unbounded data Continuous computation Real-time analytics Distributed RPC and ETL processing Request this course now! Audience Software and ETL developers Mainframe professionals Data scientists Big data analysts Hadoop professionals Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| apex | Apache Apex: Processing big data-in-motion | 21 heures | Apache Apex is a YARN-native platform that unifies stream and batch processing. It processes big data-in-motion in a way that is scalable, performant, fault-tolerant, stateful, secure, distributed, and easily operable. This instructor-led, live training introduces Apache Apex's unified stream processing architecture and walks participants through the creation of a distributed application using Apex on Hadoop. By the end of this training, participants will be able to: Understand data processing pipeline concepts such as connectors for sources and sinks, common data transformations, etc. Build, scale and optimize an Apex application Process real-time data streams reliably and with minimum latency Use Apex Core and the Apex Malhar library to enable rapid application development Use the Apex API to write and re-use existing Java code Integrate Apex into other applications as a processing engine Tune, test and scale Apex applications Audience Developers Enterprise architects Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| hypertable | Hypertable: Deploy a BigTable like database | 14 heures | Hypertable is an open-source software database management system based on the design of Google's Bigtable. In this instructor-led, live training, participants will learn how to set up and manage a Hypertable database system. By the end of this training, participants will be able to: Install, configure and upgrade a Hypertable instance Set up and administer a Hypertable cluster Monitor and optimize the performance of the database Design a Hypertable schema Work with Hypertable's API Troubleshoot operational issues Audience Developers Operations engineers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Part lecture, part discussion, exercises and heavy hands-on practice |
| matlabdl | Matlab for Deep Learning | 14 heures | In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition. By the end of this training, participants will be able to: Build a deep learning model Automate data labeling Work with models from Caffe and TensorFlow-Keras Train data using multiple GPUs, the cloud, or clusters Audience Developers Engineers Domain experts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| botsazure | Developing Intelligent Bots with Azure | 14 heures | The Azure Bot Service combines the power of the Microsoft Bot Framework and Azure functions to enable rapid development of intelligent bots. In this instructor-led, live training, participants will learn how to easily create an intelligent bot using Microsoft Azure By the end of this training, participants will be able to: Learn the fundamentals of intelligent bots Learn how to create intelligent bots using cloud applications Understand how to use the Microsoft Bot Framework, the Bot Builder SDK, and the Azure Bot Service Understand how to design bots using bot patterns Develop their first intelligent bot using Microsoft Azure Audience Developers Hobbyists Engineers IT Professionals Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| echarts | ECharts | 14 heures | eCharts is a free JavaScript library used for interactive charting and data visualization. In this instructor-led, live training, participants will learn the fundamental functionalities of ECharts as they step through the process of creating and configuring charts using ECharts. By the end of this training, participants will be able to: Understand the fundamentals of ECharts Explore and utilize the various features and configuration options in ECharts Build their own simple, interactive, and responsive charts with ECharts Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| iotemi | IoT (Internet of Things) for Entrepreneurs, Managers and Investors | 21 heures | Unlike other technologies, IoT is far more complex encompassing almost every branch of core Engineering-Mechanical, Electronics, Firmware, Middleware, Cloud, Analytics and Mobile. For each of its engineering layers, there are aspects of economics, standards, regulations and evolving state of the art. This is for the firs time, a modest course is offered to cover all of these critical aspects of IoT Engineering. Summary An advanced training program covering the current state of the art in Internet of Things Cuts across multiple technology domains to develop awareness of an IoT system and its components and how it can help businesses and organizations. Live demo of model IoT applications to showcase practical IoT deployments across different industry domains, such as Industrial IoT, Smart Cities, Retail, Travel & Transportation and use cases around connected devices & things Target Audience Managers responsible for business and operational processes within their respective organizations and want to know how to harness IoT to make their systems and processes more efficient. Entrepreneurs and Investors who are looking to build new ventures and want to develop a better understanding of the IoT technology landscape to see how they can leverage it in an effective manner. Estimates for Internet of Things or IoT market value are massive, since by definition the IoT is an integrated and diffused layer of devices, sensors, and computing power that overlays entire consumer, business-to-business, and government industries. The IoT will account for an increasingly huge number of connections: 1.9 billion devices today, and 9 billion by 2018. That year, it will be roughly equal to the number of smartphones, smart TVs, tablets, wearable computers, and PCs combined. In the consumer space, many products and services have already crossed over into the IoT, including kitchen and home appliances, parking, RFID, lighting and heating products, and a number of applications in Industrial Internet. However, the underlying technologies of IoT are nothing new as M2M communication existed since the birth of Internet. However what changed in last couple of years is the emergence of number of inexpensive wireless technologies added by overwhelming adaptation of smart phones and Tablet in every home. Explosive growth of mobile devices led to present demand of IoT. Due to unbounded opportunities in IoT business, a large number of small and medium sized entrepreneurs jumped on a bandwagon of IoT gold rush. Also due to emergence of open source electronics and IoT platform, cost of development of IoT system and further managing its sizable production is increasingly affordable. Existing electronic product owners are experiencing pressure to integrate their device with Internet or Mobile app. This training is intended for a technology and business review of an emerging industry so that IoT enthusiasts/entrepreneurs can grasp the basics of IoT technology and business. Course Objective Main objective of the course is to introduce emerging technological options, platforms and case studies of IoT implementation in home & city automation (smart homes and cities), Industrial Internet, healthcare, Govt., Mobile Cellular and other areas. Basic introduction of all the elements of IoT-Mechanical, Electronics/sensor platform, Wireless and wireline protocols, Mobile to Electronics integration, Mobile to enterprise integration, Data-analytics and Total control plane M2M Wireless protocols for IoT- WiFi, Zigbee/Zwave, Bluetooth, ANT+ : When and where to use which one? Mobile/Desktop/Web app- for registration, data acquisition and control –Available M2M data acquisition platform for IoT-–Xively, Omega and NovoTech, etc. Security issues and security solutions for IoT Open source/commercial electronics platform for IoT-Raspberry Pi, Arduino , ArmMbedLPC etc Open source /commercial enterprise cloud platform for AWS-IoT apps, Azure -IOT, Watson-IOT cloud in addition to other minor IoT clouds Studies of business and technology of some of the common IoT devices like Home automation, Smoke alarm, vehicles, military, home health etc. |
| apachemdev | Apache Mahout for Developers | 14 heures | Audience Developers involved in projects that use machine learning with Apache Mahout. Format Hands on introduction to machine learning. The course is delivered in a lab format based on real world practical use cases. |
| cassdev | Cassandra pour Développeurs | 21 heures | This course will introduce Cassandra – a popular NoSQL database. It will cover Cassandra principles, architecture and data model. Students will learn data modeling in CQL (Cassandra Query Language) in hands-on, interactive labs. This session also discusses Cassandra internals and some admin topics. Audience : Developers |
| datama | Data Mining and Analysis | 28 heures | Objective: Delegates be able to analyse big data sets, extract patterns, choose the right variable impacting the results so that a new model is forecasted with predictive results. |
| genealgo | Genetic Algorithms | 28 heures | This four day course is aimed at teaching how genetic algorithms work; it also covers how to select model parameters of a genetic algorithm; there are many applications for genetic algorithms in this course and optimization problems are tackled with the genetic algorithms. |
| IntroToAvro | Apache Avro: Data serialization for distributed applications | 14 heures | This course is intended for Developers Format of the course Lectures, hands-on practice, small tests along the way to gauge understanding |
| nlpwithr | NLP: Natural Language Processing with R | 21 heures | It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data. This course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements. By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance. Audience Linguists and programmers Format of the course Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding |
| glusterfs | GlusterFS for System Administrators | 21 heures | GlusterFS is an open-source distributed file storage system that can scale up to petabytes of capacity. GlusterFS is designed to provide additional space depending on the user's storage requirements. A common application for GlusterFS is cloud computing storage systems. In this instructor-led training, participants will learn how to use normal, off-the-shelf hardware to create and deploy a storage system that is scalable and always available. By the end of the course, participants will be able to: Install, configure, and maintain a full-scale GlusterFS system. Implement large-scale storage systems in different types of environments. Audience System administrators Storage administrators Format of the Course Part lecture, part discussion, exercises and heavy hands-on practice. |
| vespa | Vespa: Serving large-scale data in real-time | 14 heures | Vespa an open-source big data processing and serving engine created by Yahoo. It is used to respond to user queries, make recommendations, and provide personalized content and advertisements in real-time. This instructor-led, live training introduces the challenges of serving large-scale data and walks participants through the creation of an application that can compute responses to user requests, over large datasets in real-time. By the end of this training, participants will be able to: Use Vespa to quickly compute data (store, search, rank, organize) at serving time while a user waits Implement Vespa into existing applications involving feature search, recommendations, and personalization Integrate and deploy Vespa with existing big data systems such as Hadoop and Storm. Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| pythonadvml | Python for Advanced Machine Learning | 21 heures | In this instructor-led, live training, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data. By the end of this training, participants will be able to: Implement machine learning algorithms and techniques for solving complex problems Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data Push Python algorithms to their maximum potential Use libraries and packages such as NumPy and Theano Audience Developers Analysts Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| powerbiforbiandanalytics | Power BI for Business Analysts | 21 heures | Microsoft Power BI is a free Software as a Service (SaaS) suite for analyzing data and sharing insights. Power BI dashboards provide a 360-degree view of the most important metrics in one place, updated in real time, and available on all of their devices. In this instructor-led, live training, participants will learn how to use Microsoft Power Bi to analyze and visualize data using a series of sample data sets. By the end of this training, participants will be able to: Create visually compelling dashboards that provide valuable insights into data Obtain and integrate data from multiple data sources Build and share visualizations with team members Adjust data with Power BI Desktop Audience Business managers Business analystss Data analysts Business Intelligence (BI) and Data Warehouse (DW) teams Report developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| devbot | Developing a Bot | 14 heures | A bot or chatbot is like a computer assistant that is used to automate user interactions on various messaging platforms and get things done faster without the need for users to speak to another human. In this instructor-led, live training, participants will learn how to get started in developing a bot as they step through the creation of sample chatbots using bot development tools and frameworks. By the end of this training, participants will be able to: Understand the different uses and applications of bots Understand the complete process in developing bots Explore the different tools and platforms used in building bots Build a sample chatbot for Facebook Messenger Build a sample chatbot using Microsoft Bot Framework Audience Developers interested in creating their own bot Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| drlpython | Deep Reinforcement Learning with Python | 21 heures | Deep Reinforcement Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches. In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent. By the end of this training, participants will be able to: Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning Apply advanced Reinforcement Learning algorithms to solve real-world problems Build a Deep Learning Agent Audience Developers Data Scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| droolsrlsadm | Drools Rules Administration | 21 heures | This course has been prepared for people who are involved in administering corporate knowledge assets (rules, process) like system administrators, system integrators, application server administrators, etc... We are using the newest stable community version of Drools to run this course, but older versions are also possible if agreed before booking. |
| rprogadv | Advanced R Programming | 7 heures | This course is for data scientists and statisticians that already have basic R & C++ coding skills and R code and need advanced R coding skills. The purpose is to give a practical advanced R programming course to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience |
| hadoopba | Hadoop for Business Analysts | 21 heures | Apache Hadoop is the most popular framework for processing Big Data. Hadoop provides rich and deep analytics capability, and it is making in-roads in to tradional BI analytics world. This course will introduce an analyst to the core components of Hadoop eco system and its analytics Audience Business Analysts Duration three days Format Lectures and hands on labs. |
| datapro | Data Protection | 35 heures | This is an Instructor led course, and is the non-certification version of the "CDP - Certificate in Data Protection" course Those experienced in data protection issues, as well as those new to the subject, need to be trained so that their organisations are confident that legal compliance is continually addressed. It is necessary to identify issues requiring expert data protection advice in good time in order that organisational reputation and credibility are enhanced through relevant data protection policies and procedures. Objectives: The aim of the syllabus is to promote an understanding of how the data protection principles work rather than simply focusing on the mechanics of regulation. The syllabus places the Act in the context of human rights and promotes good practice within organisations. On completion you will have: an appreciation of the broader context of the Act. an understanding of the way in which the Act and the Privacy and Electronic Communications (EC Directive) Regulations 2003 work a broad understanding of the way associated legislation relates to the Act an understanding of what has to be done to achieve compliance Course Synopsis: The syllabus comprises three main parts, each sub-sections. Context - this will address the origins of and reasons for the Act together with consideration of privacy in general. Law – Data Protection Act - this will address the main concepts and elements of the Act and subordinate legislation. Application - this will consider how compliance is achieved and how the Act works in practice. |
| datavisR1 | Introduction to Data Visualization with R | 28 heures | This course is intended for data engineers, decision makers and data analysts and will lead you to create very effective plots using R studio that appeal to decision makers and help them find out hidden information and take the right decisions |
| Piwik | Getting started with Piwik | 21 heures | Audience Web analysist Data analysists Market researchers Marketing and sales professionals System administrators Format of course Part lecture, part discussion, heavy hands-on practice |
| voldemort | Voldemort: Setting up a key-value distributed data store | 14 heures | Voldemort is an open-source distributed data store that is designed as a key-value store. It is used at LinkedIn by numerous critical services powering a large portion of the site. This course will introduce the architecture and capabilities of Voldomort and walk participants through the setup and application of a key-value distributed data store. Audience Software developers System administrators DevOps engineers Format of the course Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding |
| kylin | Apache Kylin: From classic OLAP to real-time data warehouse | 14 heures | Apache Kylin is an extreme, distributed analytics engine for big data. In this instructor-led live training, participants will learn how to use Apache Kylin to set up a real-time data warehouse. By the end of this training, participants will be able to: Consume real-time streaming data using Kylin Utilize Apache Kylin's powerful features, including snowflake schema support, a rich SQL interface, spark cubing and subsecond query latency Note We use the latest version of Kylin (as of this writing, Apache Kylin v2.0) Audience Big data engineers Big Data analysts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| ApacheIgnite | Apache Ignite: Improve speed, scale and availability with in-memory computing | 14 heures | Apache Ignite is an in-memory computing platform that sits between the application and data layer to improve speed, scale and availability. In this instructor-led, live training, participants will learn the principles behind persistent and pure in-memory storage as they step through the creation of a sample in-memory computing project. By the end of this training, participants will be able to: Use Ignite for in-memory, on-disk persistence as well as a purely distributed in-memory database Achieve persistence without syncing data back to a relational database Use Ignite to carry out SQL and distributed joins Improve performance by moving data closer to the CPU, using RAM as a storage Spread data sets across a cluster to achieve horizontal scalability Integrate Ignite with RDBMS, NoSQL, Hadoop and machine learning processors Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| radvml | Advanced Machine Learning with R | 21 heures | In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application. By the end of this training, participants will be able to: Use techniques as hyper-parameter tuning and deep learning Understand and implement unsupervised learning techniques Put a model into production for use in a larger application Audience Developers Analysts Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| mlbankingr | Machine Learning for Banking (with R) | 28 heures | In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. R will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of live projects. Audience Developers Data scientists Banking professionals with a technical background Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| sparkpython | Python and Spark for Big Data (PySpark) | 21 heures | Python is a high-level programming language famous for its clear syntax and code readibility. Spark is a data processing engine used in querying, analyzing, and transforming big data. PySpark allows users to interface Spark with Python. In this instructor-led, live training, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises. By the end of this training, participants will be able to: Learn how to use Spark with Python to analyze Big Data Work on exercises that mimic real world circumstances Use different tools and techniques for big data analysis using PySpark Audience Developers IT Professionals Data Scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| apachedrillqueryoptim | Apache Drill Query Optimization | 7 heures | Apache Drill is a schema-free, distributed, in-memory columnar SQL query engine for Hadoop, NoSQL and other Cloud and file storage systems. The power of Apache Drill lies in its ability to join data from multiple data stores using a single query. Apache Drill supports numerous NoSQL databases and file systems, including HBase, MongoDB, MapR-DB, HDFS, MapR-FS, Amazon S3, Azure Blob Storage, Google Cloud Storage, Swift, NAS and local files. Apache Drill is the open source version of Google's Dremel system which is available as an infrastructure service called Google BigQuery. In this instructor-led, live training, participants will learn how to optimize and debug Apache Drill to improve the performance of queries on very large data sets. The course begins with an architectural overview and feature comparison between Apache Drill and other interactive data analysis tools. Participants then step through a series of interactive, hands-on practice sessions that include installation, configuration, performance evaluation, query optimization, data partitioning, and debugging of an Apache Drill instance in a live lab environment. By the end of this training, participants will be able to: Install and configure Apache Drill Understand Apache Drill's architecture and features Understand how Apache Drills receives and executes queries Optimize Drill queries for distributed SQL execution Debug Apache Drill Audience Developers Systems administrators Data analysts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Notes To request a customized training for this course, please contact us to arrange. |
| d2dbdpa | From Data to Decision with Big Data and Predictive Analytics | 21 heures | Audience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery Mode During the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software used All software used is updated each time the course is run so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning. |
| optaprac | OptaPlanner en Pratique | 21 heures | This course uses a practical approach to teaching OptaPlanner. It provides participants with the tools needed to perform the basic functions of this tool. |
| dataar | Data Analytics With R | 21 heures | R is a very popular, open source environment for statistical computing, data analytics and graphics. This course introduces R programming language to students. It covers language fundamentals, libraries and advanced concepts. Advanced data analytics and graphing with real world data. Audience Developers / data analytics Duration 3 days Format Lectures and Hands-on |
| osqlide | Oracle SQL Intermediate - Data Extraction | 14 heures | The objective of the course is to enable participants to gain a mastery of how to work with the SQL language in Oracle database for data extraction at intermediate level. |
| surveyp | Research Survey Processing | 28 heures | This four day course walks you from the point you design your research surveys to the tme where you gather and collect the findings of the survey. The course is based on Excel and Matlab. You will learn how to design the survey form and what the suitable data fields should be, and how to process extra data information when needed. The course will show you the way the data is entered and how to validate and correct wrong data values. At the end the data analysis will be conducted in a variety of ways to ensure the effectiveness of the data gathered and to find out hidden trends and knowledge within this data. A number of case studies will be carried out during the course to make sure all the concepts have been well understood. |
| neo4j | Beyond the relational database: neo4j | 21 heures | Relational, table-based databases such as Oracle and MySQL have long been the standard for organizing and storing data. However, the growing size and fluidity of data have made it difficult for these traditional systems to efficiently execute highly complex queries on the data. Imagine replacing rows-and-columns-based data storage with object-based data storage, whereby entities (e.g., a person) could be stored as data nodes, then easily queried on the basis of their vast, multi-linear relationship with other nodes. And imagine querying these connections and their associated objects and properties using a compact syntax, up to 20 times lighter than SQL. This is what graph databases, such as neo4j offer. In this hands-on course, we will set up a live project and put into practice the skills to model, manage and access your data. We contrast and compare graph databases with SQL-based databases as well as other NoSQL databases and clarify when and where it makes sense to implement each within your infrastructure. Audience Database administrators (DBAs) Data analysts Developers System Administrators DevOps engineers Business Analysts CTOs CIOs Format of the course Heavy emphasis on hands-on practice. Most of the concepts are learned through samples, exercises and hands-on development. |
| sphynx | Sphinx: Developing speech-enabled applications | 21 heures | Speech technology is increasingly being used to create highly interactive, voice-activated applications. From voice-control, to smart assistants, to speech transcription and translation, to closed-captioning and language learning, the improved accuracy and processing speed of this technology is enhancing the quality of applications and delivering greater user experiences. In this course we use the open-source Sphinx toolkit (aka CMU Sphinx) to demonstrate and model various types of speech-enabled applications. By the end of the course participants should have a solid grasp of the tools and techniques needed to apply speech technology to their own applications. Sphinx 4 will be the basis for this training, however, coverage of Sphinx 3 can also be arranged. Audience Software developers and programmers Format of the course Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding |
| matlabfundamentalsfinance | MATLAB Fundamentals + MATLAB for Finance | 35 heures | This course provides a comprehensive introduction to the MATLAB technical computing environment + an introduction to using MATLAB for financial applications. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include: Working with the MATLAB user interface Entering commands and creating variables Analyzing vectors and matrices Visualizing vector and matrix data Working with data files Working with data types Automating commands with scripts Writing programs with logic and flow control Writing functions Using the Financial Toolbox for quantitative analysis |
| snorkel | Snorkel: Rapidly process training data | 7 heures | Snorkel is a system for rapidly creating, modeling, and managing training data. It focuses on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain. In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel. By the end of this training, participants will be able to: Programmatically create training sets to enable the labeling of massive training sets Train high-quality end models by first modeling noisy training sets Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| encogadv | Encog: Advanced Machine Learning | 14 heures | Encog is an open-source machine learning framework for Java and .Net. In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models. By the end of this training, participants will be able to: Implement different neural networks optimization techniques to resolve underfitting and overfitting Understand and choose from a number of neural network architectures Implement supervised feed forward and feedback networks Audience Developers Analysts Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| mlbankingpython_ | Machine Learning for Banking (with Python) | 21 heures | Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| mlfinancepython | Machine Learning for Finance (with Python) | 21 heures | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. By the end of this training, participants will be able to: Understand the fundamental concepts in machine learning Learn the applications and uses of machine learning in finance Develop their own algorithmic trading strategy using machine learning with Python Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| dremio | Dremio for Self-Service Data Analysis | 21 heures | Dremio is an open-source "self-service data platform" that accelerates the querying of different types of data sources. Dremio integrates with relational databases, Apache Hadoop, MongoDB, Amazon S3, ElasticSearch, and other data sources. It supports SQL and provides a web UI for building queries. In this instructor-led, live training, participants will learn how to install, configure and use Dremio as a unifying layer for data analysis tools and the underlying data repositories. By the end of this training, participants will be able to: Install and configure Dremio Execute queries against multiple data sources, regardless of location, size, or structure Integrate Dremio with BI and data sources such as Tableau and Elasticsearch Audience Data scientists Business analysts Data engineers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Notes To request a customized training for this course, please contact us to arrange. |
| bldrools | Managing Business Logic with Drools | 21 heures | This course is aimed at enterprise architects, business and system analysts, technical managers and developers who want to apply business rules to their solutions. This course contains a lot of simple hands-on exercises during which the participants will create working rules. Please refer to our other courses if you just need an overview of Drools. This course is usually delivered on the newest stable version of Drools and jBPM, but in case of a bespoke course, can be tailored to a specific version. |
Upcoming Courses
| Formation | Date Formation | Prix [A distance / Classe] |
|---|---|---|
| Fairsec: Setting up a CNN-based machine translation system - Toulouse, centre ville | lun, 2018-06-11 09:30 | 1500EUR / 1900EUR |
| Pattern Matching - Tours, Parc Honoré de Balzac | lun, 2018-06-11 09:30 | 3500EUR / 4100EUR |
| Data Mining - Formation Online | mer, 2018-06-13 09:30 | 5250EUR / 6050EUR |
| Introduction à l’intelligence artificielle : outils et enjeux - Montpellier, Garosud | mer, 2018-06-20 09:30 | 3000EUR / 3600EUR |
| Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple - Lyon, Gare Lyon Part-Dieu | lun, 2018-06-25 09:30 | 7000EUR / 8000EUR |
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