Python for Matlab Users Training Course

This intensive, hands-on course explores advanced Python techniques, engineering best practices, and widely used design patterns to help you build Python applications that are maintainable, testable, and high-performing. It places emphasis on modern tooling, type hinting, concurrency models, architectural patterns, and deployment-ready workflows.

Delivered as instructor-led live training (online or onsite), this program targets intermediate to advanced Python developers aiming to adopt professional practices and patterns for production-grade Python systems.

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

• Enhance code reliability by applying Python typing, dataclasses, and type-checking.
• Structure robust applications using design patterns and architectural principles.
• Correctly implement concurrency and parallelism utilizing asyncio and multiprocessing.
• Develop well-tested code through the use of pytest, property-based testing, and CI pipelines.
• Profile, optimize, and harden Python applications for production environments.
• Package, distribute, and deploy Python projects employing modern tools and containers.

Course Format

• Interactive lectures paired with concise demonstrations.
• Daily hands-on labs and coding exercises.
• A capstone mini-project that integrates patterns, testing, and deployment.

Course Customization Options

• To request customized training or focus on specific areas (data, web, or infrastructure), please contact us to make arrangements.

This course delivers practical engineering methodologies for designing, building, testing, and deploying agentic (autonomous) systems using Python. It explores the agent loop, tool integrations, memory and state management, orchestration patterns, safety controls, and production considerations.

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced ML engineers, AI developers, and software engineers seeking to build robust, production-ready autonomous agents using Python.

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

• Design and implement the agent loop and decision-making workflows.
• Integrate external tools and APIs to expand agent capabilities.
• Implement short-term and long-term memory architectures for agents.
• Coordinate multi-step orchestrations and agent composability.
• Apply safety, access control, and observability best practices for deployed agents.

Course Format

• Interactive lecture and discussion.
• Hands-on labs building agents with Python and popular SDKs.
• Project-based exercises that produce deployable prototypes.

Course Customization Options

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

This course offers a practical exploration of Data Science and AI through Python, empowering professionals with the capabilities to analyze data, construct machine learning models, and implement AI-powered applications within business environments. It addresses CRISP-DM methodologies, statistical analysis, supervised and unsupervised learning, deep learning with Tensorflow, natural language processing, big data analytics via Spark, and data-driven storytelling. This program is ideal for beginners aiming to obtain a Python data science certification and acquire career-ready analytics skills.

Artificial Intelligence with Python involves building intelligent systems by leveraging Python’s robust ecosystem of AI and machine learning libraries.

This instructor-led live training, available online or onsite, is designed for intermediate Python programmers looking to design, implement, and deploy AI solutions using Python.

Upon completing this training, participants will be able to:

• Implement AI algorithms using Python’s core AI libraries.
• Work with supervised, unsupervised, and reinforcement learning models.
• Integrate AI solutions into existing applications and workflows.
• Evaluate model performance and optimize for accuracy and efficiency.

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

• Interactive lecture and discussion.
• Extensive exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

This instructor-led live training in France (online or onsite) is designed for business analysts aiming to automate trading via algorithmic methods, Python, and R.

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

• Use algorithms to rapidly buy and sell securities at specialized intervals.
• Reduce trade-related costs through algorithmic trading.
• Automatically monitor stock prices and execute trades.

This course empowers programmers and data analysts with the essential techniques needed to construct machine learning solutions entirely from scratch using Python. It explores the core principles of supervised learning (including classification and regression) and unsupervised learning (such as clustering and anomaly detection), alongside advanced neural network architectures. Participants will examine proven methods for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The curriculum supports professionals in implementing practical ML models, assessing algorithmic limitations, and completing applied projects designed for real-world problem-solving.

This instructor-led, live training in France (online or onsite) is designed for intermediate-level Python developers who aim to enhance their Python development experience using AWS Cloud9.

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

• Set up and configure AWS Cloud9 for Python development.
• Understand the AWS Cloud9 IDE interface and its features.
• Write, debug, and deploy Python applications in AWS Cloud9.
• Collaborate with other developers using the AWS Cloud9 platform.
• Integrate AWS Cloud9 with other AWS services for advanced deployments.

Course Overview

This practical training program is tailored for data engineering professionals seeking to develop applied skills in artificial intelligence, Python, and large language models. The curriculum emphasizes real-world use cases, including model utilization, prompt engineering, and the creation of AI-driven solutions. Participants will engage in progressive exercises that advance from foundational concepts to the construction of deployable AI workflows.

Training Format

• In-person classroom instruction

• Instructor-led sessions with guided practice

• Interactive discussions and real-world case studies

• Daily hands-on exercises

Course Objectives

• Grasp core AI and machine learning concepts applicable to modern solutions

• Enhance Python proficiency for AI development and data workflows

• Comprehend the mechanics of large language models and learn to leverage them effectively

• Design and optimize prompts to ensure reliable outputs

• Develop end-to-end AI solutions utilizing APIs and frameworks

• Integrate AI capabilities into data engineering pipelines

This instructor-led, live training in France (online or onsite) is designed for data scientists and software engineers who wish to use Dask with the Python ecosystem to build, scale, and analyze large datasets.

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

• Configure the environment to begin building big data processing solutions using Dask and Python.
• Explore the features, libraries, tools, and APIs available in Dask.
• Understand how Dask accelerates parallel computing in Python.
• Learn how to scale the Python ecosystem (including NumPy, SciPy, and Pandas) using Dask.
• Optimize the Dask environment to maintain high performance when handling large datasets.

This instructor-led, live training in France (online or onsite) is aimed at intermediate-level Python developers and data analysts who wish to enhance their skills in data analysis and manipulation using Pandas and NumPy.

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

• Set up a development environment that includes Python, Pandas, and NumPy.
• Create a data analysis application using Pandas and NumPy.
• Perform advanced data wrangling, sorting, and filtering operations.
• Conduct aggregate operations and analyze time series data.
• Visualize data using Matplotlib and other visualization libraries.
• Debug and optimize their data analysis code.

This instructor-led live training, offered online or onsite, is intended for developers aiming to leverage the FARM (FastAPI, React, and MongoDB) stack to build dynamic, high-performance, and scalable web applications.

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

• Set up the necessary development environment that integrates FastAPI, React, and MongoDB.
• Understand the key concepts, features, and benefits of the FARM stack.
• Learn how to build REST APIs with FastAPI.
• Learn how to design interactive applications with React.
• Develop, test, and deploy applications (front end and back end) using the FARM stack.

This instructor-led live training in France (online or onsite) targets developers who wish to use FastAPI with Python to build, test, and deploy RESTful APIs more easily and quickly.

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

• Set up the necessary development environment to develop APIs with Python and FastAPI.
• Create APIs quicker and easier using the FastAPI library.
• Learn how to create data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to a database using SQLAlchemy.
• Implement security and authentication in APIs using the FastAPI tools.
• Build container images and deploy web APIs to a cloud server.

This instructor-led live training in France (online or onsite) targets data scientists who want to use TensorFlow to analyze potential fraud data.

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

• Create a fraud detection model in Python and TensorFlow.
• Build linear regressions and linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

This course aims to equip participants with general proficiency in applying Machine Learning methods in real-world scenarios. By leveraging the Python programming language and its extensive ecosystem of libraries, and supported by a wide range of practical examples, the course demonstrates how to utilize the essential components of Machine Learning. Participants will learn to make informed data modeling decisions, interpret algorithm outputs, and validate results effectively.

Our objective is to empower you with the confidence to understand and apply the core tools of the Machine Learning toolkit, while helping you steer clear of common pitfalls associated with Data Science applications.

This instructor-led, live training in France (online or onsite) is designed for network engineers who want to maintain, manage, and design computer networks using Python.

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

• Optimize and leverage Paramiko, Netmiko, Napalm, Telnet, and pyntc for network automation with Python.
• Master multi-threading and multiprocessing in network automation.
• Use GNS3 and Python for network programming.