Module 1 – Introduction to Python and AI Assistants

• Overview of the language and its scientific applications
• Installing Python and the code editor (VS Code or Jupyter recommended for AI integration)
• Introduction to AI Coding Tools: Setting up an assistant (e.g., Gemini, Copilot) and core prompting principles for developers
• First steps with the Python console, scripts, and generating your initial code using AI

Module 2 – Programming Fundamentals

• Variables, data types, and type conversion
• Strings, numbers, and booleans
• Arithmetic and logical operations
• AI Practice: Using the assistant to generate examples of data type manipulation and understanding conversion errors (TypeError)

Module 3 – Control Structures and Algorithmic Logic

• Conditions (if, elif, else)
• Loops (for, while)
• Indentation principles and best practices
• AI Practice: Asking the AI to translate a textual algorithm into Python control structures, and using the assistant to catch indentation errors (IndentationError)

Module 4 – Functions, Modularity, and Documentation

• Creating and calling a function (passing parameters and returning values)
• Structuring code into logical blocks
• AI Practice:
  • Writing prompts to generate reusable functions
  • Using AI to automatically generate comments and documentation (docstrings) compliant with PEP 8 standards

Module 5 – Introduction to Object-Oriented Programming (Optional)

• Understanding the concept of objects in Python
• Creating a class and instantiating simple objects
• AI Practice: Using the assistant to model simple object relationships and generate the structural code for a class

Module 6 – File Manipulation and Data Handling

• Reading and writing text files (TXT, CSV)
• Parsing and processing data line by line
• AI Practice: Asking the assistant to generate quick data cleaning or formatting scripts based on a provided file sample

Module 7 – AI-Augmented Practical Project

• Creating a complete project combining variables, loops, functions, and file manipulation
• Prompt Engineering for Debugging: Learning how to submit error codes to the AI to get a diagnosis and refactoring suggestions
• Corrections, AI-driven automated code reviews, and personalized instructor feedback

Introduction to Python Training: Finance and Time Series

Module 1: Introduction to Python for Finance

• Overview of Python as a programming tool for finance.
• Install and configure the Python environment with financial libraries.
• Manipulate financial data, data types, and basic operations.

Module 2: Financial Data Analysis with Pandas

• Introduction to the Pandas library for data analysis.
• Load financial data from various sources (CSV files, databases, etc.).
• Clean and preprocess data for in-depth analysis.
• Perform grouping, filtering, and statistical calculations on time series.

Module 3: Financial Data Visualization with Matplotlib and Seaborn

• Introduction to Matplotlib and Seaborn for data visualization.
• Create charts such as price curves, return histograms, and candlestick plots.
• Customize visualizations for effective presentation of financial data.

Module 4: Financial Time Series Analysis

• Understand key concepts in financial time series (volatility, trends, seasonality, etc.).
• Apply time series models such as Moving Average, Autoregression (AR), ARMA, and ARIMA.
• Evaluate and interpret model performance.

Module 5: Predicting Financial Prices with Machine Learning

• Introduction to basic machine learning and regression concepts.
• Use Machine Learning libraries such as Scikit-Learn to predict financial prices.
• Compare regression model performance to achieve accurate predictions.

Module 6: Practical Finance Applications with Python

• Apply acquired skills to solve real-world financial problems.
• Create investment strategies using financial time series analysis.
• Implement risk and portfolio analysis to support informed decision-making.

Introduction to the Python and Data Science Course – Block 1

• Variable types (integer, float, boolean, etc.) and basic operators;
• Data structures (list, tuple, dictionary, etc.);
• Flow control (if-else, try-except-finally);
• Loops (for, while);
• Combining flow control and loops;
• Functions and methods (function(object) and object.method());
• Importing external modules to access additional functions and methods;
• Syntax principles and best practices.

Introduction to Python – Block 2

• Managing modules;
• Administering the operating system;
• Extracting data from the web (web scraping);
• Cleaning and transforming data using the regex module;
• Working with time using the datetime module.

Introduction to Data Science

• Jupyter Notebook, Spyder, and other tools;
• Introduction to NumPy;
• Introduction to Pandas – Series objects;
• Introduction to Pandas – DataFrame objects;
• • Extra: Introduction to Pandas – Instructions;
  • Extra: Tidy Data;
• Groupby operations and aggregations;
• Data collection from Web APIs and analysis;
• Data visualization;
• • Extra: Geospatial visualization.

tool

Install the Anaconda distribution

Python for scientific computing: Numpy

Introduction to Numpy
Create ndarray objects
Data selection
Add, edit, delete items
Use numpy functions
Enter exit

Manipulating data with Pandas

Series objects
DataFrames objects
Data selection
Aggregation functions
Merge, Join, Remodeling
Use lambda functions
Make a dynamic crossover (Pivot Table)
Manipulate excel data (csv) and json

Visualize data with Matplotlib

2D curve display
Point cloud display
Histogram display
Web API

Request to an API
Get the answer
Treat the answer
Application: API Twitter, analyze and visualize in time
->

A data scientist, Hugues explores data collected within organizations and develops analyses and forecasts, or collaborates on projects aimed at extracting value from collected data. His projects include, among others, customer segmentation, map-based data visualization, transaction matching, and subscription lifetime analysis.

He also provides continuing and private training on the tools and methods he uses in his projects. His courses cover introductory R and Python programming, as well as their applications in data science.

He also volunteers in the development of extracurricular activities focused on learning programming with microcontrollers. The goal is to inspire young people to pursue careers in science and technology.

Module 1 – Introduction to Machine Learning

• Understand Artificial Intelligence and Machine Learning
• Difference between AI, Machine Learning, and Deep Learning
• Understand models, training, and predictions
• Understand features and labels
• Supervised and unsupervised learning
• Classification, regression, and clustering
• Examples of Machine Learning use cases in organizations

Module 2 – Machine Learning on AWS

• Overview of the AWS Machine Learning ecosystem
• Introduction to Amazon SageMaker AI
• Role of Amazon S3 in a Machine Learning project
• Introduction to IAM roles and permissions
• Introduction to Amazon CloudWatch for monitoring
• Understand the architecture of a Machine Learning solution on AWS

Module 3 – Preparing the AWS Environment

• Access the AWS Management Console
• Select an AWS Region
• Create and configure an Amazon S3 bucket
• Upload a dataset to Amazon S3
• Access Amazon SageMaker AI
• Configure the required permissions
• Explore the SageMaker working environment

Module 4 – Understanding and Preparing Data

• Identify the data used for model training
• Understand rows, columns, and variables
• Identify numerical and categorical variables
• Identify the target variable
• Handle missing values
• Identify and remove duplicates
• Understand outliers
• Select relevant features

Module 5 – Transforming Data for Machine Learning

• Clean a dataset
• Transform data types
• Encode categorical variables
• Normalize data
• Introduction to Feature Engineering
• Create a dataset ready for model training
• Discover data preparation tools in SageMaker

Module 6 – Preparing Training and Test Data

• Understand the purpose of training data
• Understand the purpose of validation data
• Understand the purpose of test data
• Split a dataset into multiple subsets
• Understand model generalization

Module 7 – Training a Model with Amazon SageMaker AI

• Understand how Machine Learning algorithms work
• Select an algorithm appropriate for the problem
• Understand SageMaker Training Jobs
• Configure a training job in SageMaker
• Understand hyperparameters
• Select computing resources
• Launch a training job
• Monitor the training process
• Retrieve the trained model

Module 8 – Evaluating Model Performance

• Understand why models must be evaluated
• Measure Accuracy
• Understand Precision
• Understand Recall
• Discover the F1 Score
• Understand the confusion matrix
• Compare predictions with expected results
• Interpret model performance

Module 9 – Understanding Underfitting and Overfitting

• Understand Underfitting
• Understand Overfitting
• Identify a model that does not generalize well
• Understand how data affects model performance
• Understand the impact of hyperparameters
• Improve a model progressively

Module 10 – Optimizing a Machine Learning Model

• Compare multiple models
• Adjust hyperparameters
• Improve the features used by the model
• Introduction to Hyperparameter Tuning
• Retrain a model
• Compare performance results
• Select the best-performing model

Module 11 – Deploying a Model with Amazon SageMaker AI

• Understand the concept of inference
• Create a deployable model
• Understand SageMaker endpoints
• Configure an endpoint
• Deploy a model
• Send new data to the model
• Retrieve predictions
• Discover real-time inference
• Introduction to batch and serverless inference

Module 12 – Integrating a Model into an AWS Application

• Understand how an application consumes a Machine Learning model
• Call a SageMaker endpoint using Python
• Introduction to AWS Lambda
• Introduction to Amazon API Gateway
• Understand an API Gateway, Lambda, and SageMaker architecture
• Test predictions from an application

Module 13 – Introduction to MLOps on AWS

• Understand the Machine Learning model lifecycle
• Version Machine Learning models
• Introduction to SageMaker Model Registry
• Register different versions of a model
• Understand model approval workflows
• Manage model deployment to production
• Understand the model retraining process

Module 14 – Monitoring a Model in Production

• Introduction to Amazon CloudWatch
• Review endpoint metrics
• Review logs
• Understand Data Drift
• Understand Model Drift
• Introduction to SageMaker Model Monitor
• Identify model performance degradation
• Determine when a model should be retrained

Module 15 – Security and Cost Management

• Understand IAM roles used by SageMaker
• Apply the principle of least privilege
• Control access to data stored in Amazon S3
• Understand data protection
• Identify the main SageMaker cost drivers
• Understand Training Job costs
• Understand endpoint costs
• Remove unused AWS resources after labs

Module 16 – Introduction to Generative AI on AWS

• Understand the difference between traditional Machine Learning and generative AI
• Understand Foundation Models
• Understand Large Language Models
• Introduction to Amazon Bedrock
• Understand the concept of prompting
• Introduction to embeddings
• Introduction to Retrieval-Augmented Generation (RAG)
• Identify use cases for SageMaker AI and Amazon Bedrock

Module 17 – Hands-on Workshop: End-to-End Machine Learning Project

• Import a dataset into Amazon S3
• Explore and prepare the data
• Split the data into training and test datasets
• Configure Amazon SageMaker AI
• Train a Machine Learning model
• Evaluate model performance
• Improve the model
• Deploy the model to a SageMaker endpoint
• Send new data to the deployed model
• Generate and interpret predictions
• Review metrics and logs
• Remove AWS resources used during the workshop

Module 1: Introduction to Artificial Intelligence

• Overview and Presentation with Q&A (1.5 Hours)
• Definition and Evolution of AI:
  • Terminology and Evolution
  • From Early Algorithms to Neural Networks
  • Deep Learning and Generative AI
  • Narrow AI, Artificial General Intelligence, and Superintelligence
  • Examples of Applications Across Various Fields
  • Factors Supporting AI:
  • Algorithmic Structures and Heuristics
  • Hardware Components: CPUs, GPUs, and TPUs
  • Programming Languages: R, Python, C++, Rust, and Mojo
  • Software and Frameworks
• Machine Learning Overview:
  • Main Categories of Models
  • Supervised and Unsupervised Learning
  • Applications and Fields of Use
• Deep Learning Overview:
  • Differences Between Deep Learning and Machine Learning
  • Concepts and Models
  • Applications and Fields of Use
  • Specific Characteristics of Natural Language Processing
• Generative AI Overview:
  • Language, Image, and Multimodal Models
  • Applications and Products Available on the Market
  • Example: Google Gemini
• Issues and Risks:
  • Ethics and Bias
  • Privacy and Environmental Impact
  • Risk of Dystopian Outcomes

Module 2: Artificial Intelligence Demonstrations

• Overview and Presentation with Q&A (1.5 Hours)
• DevOps and AI Pipelines:
  • Data Preprocessing
  • Training and Optimization
  • Deployment
• Machine Learning in Practice:
  • Demonstration with Scikit-Learn
  • User-Friendly Approach with PyCaret
  • Note: Source Code Provided Through Google Colab
  • Deep Learning in Practice:
  • Demonstration with NumPy
  • Using PyTorch
  • Note: Source Code Provided Through Google Colab
• Generative AI:
  • Exploring Large Language Models and Multimodal Models
  • Fine-Tuning and Autonomous Agents
  • Note: Practical Work with Google Gemini in the Following Module

Module 3: Using Google Gemini

• Hands-On Experimentation with Google Colab (3.5 Hours)
• Using the Chatbot:
  • Writing Simple Prompts
  • Tips for Python and Other Programming Prompts
• Using the Python API:
  • Designing Advanced Prompts
  • Code Optimization and Unit Testing
• Using Google AI Studio:
  • Fine-Tuning and Retrieval-Augmented Generation
  • Building an Autonomous Agent
  • Experimental Projects with Python and Other Languages

Follow-Up Training with OpenAI

OpenAI Training for Developers

Module 1: Introduction to Artificial Intelligence

• Overview and Q&A session (1h30)
• Definition and evolution of AI:
  • Terminology and evolution
  • From early algorithms to neural networks
  • Deep Learning and Generative AI
  • Weak, General, and Superintelligent AI
  • Examples of applications across sectors
• Factors supporting AI:
  • Algorithmic and heuristic structures
  • Hardware aspects (CPU, GPU, TPU)
  • Languages (R, Python, C++, Rust, Mojo)
  • Software and frameworks
• Introduction to Machine Learning:
  • Main model categories
  • Supervised and unsupervised learning
  • Applications and use cases
• Introduction to Deep Learning:
  • Differences from Machine Learning
  • Concepts and models
  • Applications and use cases
• Natural Language Processing features
• Overview of Generative AI:
  • Language, image, and multimodal models
  • Market applications and products
  • Example: Google Gemini
• Issues and risks:
  • Ethics and bias
  • Privacy and environmental impact
  • Risk of dystopian outcomes

Module 2: AI Demonstration

• Overview and Q&A session (1h30)
• DevOps and AI pipelines:
  • Data preprocessing
  • Training and optimization
  • Deployment
• Machine Learning in practice:
  • Demo with Scikit-Learn
  • User-friendly approach with PyCaret
  • Note: Source code via Google Colab
• Deep Learning in practice:
  • Demo with Numpy
  • Using PyTorch
  • Note: Source code via Google Colab
• Generative AI:
  • Exploring large language and multimodal models
  • Fine-tuning and autonomous agents
  • Note: Hands-on in the next module with Google Gemini

Module 3: Using Google Gemini

• Hands-on experimentation with Google Colab (3h30)
• Using the chatbox:
  • Crafting simple prompts
  • Tips for Python and other technical prompts
• Using the Python API:
  • Designing advanced prompts
  • Code optimization and unit testing
• Using Gemini Studio:
  • Fine-tuning and Retrieval-Augmented Generation
  • Building an autonomous agent
  • Experimental projects with Python and more

Follow-up training with OpenAI

OpenAI Training for Developers