Machine Learning and Big Data Training Course

This instructor-led live training in France (online or onsite) is intended for data scientists and software engineers who wish to apply AdaBoost for developing boosting algorithms in machine learning using Python.

By the end of this training, participants will be able to:

• Set up the required development environment to commence building machine learning models with AdaBoost.
• Comprehend the ensemble learning approach and learn how to implement adaptive boosting.
• Discover how to construct AdaBoost models to enhance machine learning algorithms in Python.
• Utilize hyperparameter tuning to boost the accuracy and performance of AdaBoost models.

This instructor-led live training in France (online or onsite) is designed for data scientists and non-technical professionals who wish to utilize Auto-Keras to automate the selection and optimization of machine learning models.

By the end of this training, participants will be able to:

• Automate the process of training highly efficient machine learning models.
• Automatically search for the best parameters for deep learning models.
• Build highly accurate machine learning models.
• Use the power of machine learning to solve real-world business problems.

This instructor-led, live training (available online or onsite) is designed for technical professionals with a background in machine learning who want to optimize models for identifying complex patterns in big data using AutoML frameworks.

This instructor-led, live training in France (online or onsite) targets participants with varying levels of expertise who aim to utilize Google's AutoML platform to develop customized chatbots for diverse applications.

Upon completion of this training, participants will be able to:

• Grasp the fundamentals of chatbot development.
• Navigate the Google Cloud Platform and access AutoML.
• Prepare data for training chatbot models.
• Train and assess custom chatbot models using AutoML.
• Deploy and integrate chatbots into various platforms and channels.
• Monitor and optimize chatbot performance over time.

This instructor-led, live training in France (online or on-site) 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.

By the end of this training, participants will be able to:

• Apply core statistical methods to pattern recognition.
• Use key models like neural networks and kernel methods for data analysis.
• Implement advanced techniques for complex problem-solving.
• Improve prediction accuracy by combining different models.

This instructor-led live training in France (online or onsite) is designed for data scientists and data analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities.

Upon completion of this training, participants will be able to:

• Import datasets into DataRobot to analyze, assess, and perform quality checks on data.
• Construct and train models to identify key variables and achieve prediction goals.
• Interpret models to derive actionable insights that support business decision-making.
• Monitor and manage models to ensure optimal prediction performance.

This instructor-led, live training in France (online or onsite) is designed for data scientists, data analysts, and developers interested in exploring AutoML products and features to create and deploy custom ML training models with minimal effort.

By the end of this training, participants will be able to:

• Explore the AutoML product line to implement various services for different data types.
• Prepare and label datasets to create custom ML models.
• Train and manage models to produce accurate and fair machine learning models.
• Make predictions using trained models to meet business objectives and needs.

This instructor-led, live training in France (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.

By the end of this training, participants will be able to:

• Install and configure Kubeflow on premise and in the cloud using AWS EKS (Elastic Kubernetes Service).
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run entire machine learning pipelines on diverse architectures and cloud environments.
• Using Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in France (online or onsite) is designed for engineers who want to deploy Machine Learning workloads to an AWS EC2 server.

By the conclusion of this training, participants will be capable of:

• Installing and configuring Kubernetes, Kubeflow, and other necessary software on AWS.
• Utilizing EKS (Elastic Kubernetes Service) to streamline the initialization of a Kubernetes cluster on AWS.
• Creating and deploying a Kubernetes pipeline to automate and manage ML models in production.
• Training and deploying TensorFlow ML models across multiple GPUs and machines operating in parallel.
• Using other AWS managed services to expand an ML application.

This instructor-led live training, available online or onsite, is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process.

By the end of this training, participants will be able to:

• Install and configure MLflow and related ML libraries and frameworks.
• Appreciate the importance of trackability, reproducability and deployability of an ML model
• Deploy ML models to different public clouds, platforms, or on-premise servers.
• Scale the ML deployment process to accommodate multiple users collaborating on a project.
• Set up a central registry to experiment with, reproduce, and deploy ML models.

This instructor-led live training, offered online or onsite, is intended for developers who aim to use Google’s ML Kit to build machine learning models optimized for mobile device processing.

Upon completion of this training, participants will be able to:

• Set up the necessary development environment to start developing machine learning features for mobile apps.
• Integrate new machine learning technologies into Android and iOS apps using the ML Kit APIs. 
• Enhance and optimize existing apps using the ML Kit SDK for on-device processing and deployment.

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.

Format of the Course also allows for the evaluation of participants.

• This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.

This instructor-led, live training in France (online or onsite) is designed for data scientists and software engineers who wish to use Random Forest to build machine learning algorithms for large datasets.

Upon completion of this training, participants will be able to:

• Set up the necessary development environment to start building machine learning models with Random Forest.
• Understand the advantages of Random Forest and how to implement it to resolve classification and regression problems.
• Learn how to handle large datasets and interpret multiple decision trees in Random Forest.
• Evaluate and optimize machine learning model performance by tuning the hyperparameters.

This instructor-led, live training in France (online or onsite) is designed for intermediate-level data analysts who wish to learn how to use RapidMiner to estimate and project values and utilize analytical tools for time series forecasting.

By the end of this training, participants will be able to:

• Learn to apply the CRISP-DM methodology, select appropriate machine learning algorithms, and enhance model construction and performance.
• Use RapidMiner to estimate and project values, and utilize analytical tools for time series forecasting.

RapidMiner is an open-source data science software platform designed for rapid application prototyping and development. It provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.

In this instructor-led live training, participants will learn how to leverage RapidMiner Studio for data preparation, machine learning, and the deployment of predictive models.

By the end of this training, participants will be able to:

• Install and configure RapidMiner
• Prepare and visualize data with RapidMiner
• Validate machine learning models
• Mashup data and create predictive models
• Operationalize predictive analytics within a business process
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Format of the Course also allows for the evaluation of participants.

• A blend of lecture, discussion, exercises, and extensive hands-on practice

Note

• To request a customized training for this course, please contact us to arrange.