https://hub.imt-atlantique.fr/datascience-toolkit/courses/index.html Presentation & objectives These two complementary lessons introduce the concepts needed to understand the practical case studies. The first develops the foundations for selecting and evaluating machine learning methods; the second explains how exploratory work evolves into maintainable Python pipelines. By the end of these lessons, you will be able to: compare supervised and unsupervised learning methods; select appropriate validation, tuning, and evaluation strategies; interpret model results and statistical evidence carefully; identify the limitations of notebook-based workflows; explain how modularity, testing, and separation of concerns support reliable ML systems. Lessons Machine Learning Techniques: From Theory to Practice — This one-hour lesson develops the conceptual foundation for distinguishing learning problems, comparing common algorithms, applying cross-validation and hyperparameter tuning, selecting evaluation metrics, and interpreting p-values without confusing statistical evidence with effect size. Hugo en-us https://hub.imt-atlantique.fr/datascience-toolkit/courses/lesson_1/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/courses/lesson_1/index.html Duration1h AI Allowed This course introduces the main families of machine learning techniques, including unsupervised learning, supervised learning, cross-validation, hyperparameter tuning, and model evaluation. It was prepared by IMT Atlantique for the Data Science Toolkit and Applications course. Table of contents Introduction to Machine Learning Unsupervised Learning: Finding Hidden Patterns Supervised Learning: Predicting the Future from the Past Cross-Validation: Ensuring Robust Models Hyperparameter Tuning: Fine-Tuning Your Model Evaluation Metrics: Measuring Model Performance Focus on P-value Conclusion and Next Steps Appendix: Cheat Sheets 1. Introduction to Machine Learning 1.1 What is Machine Learning? Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables systems to learn from data and improve their performance over time without being explicitly programmed. It is a core component of Data Science, allowing us to build models that can: https://hub.imt-atlantique.fr/datascience-toolkit/courses/lesson_2/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/courses/lesson_2/index.html Duration30 min AI Allowed Introduction This foundational lesson explores the critical transition from exploratory data science to production-ready machine learning systems. As data scientists, we often begin our work in Jupyter notebooks, which excel at rapid experimentation and iterative analysis. However, the journey from prototype to production requires a fundamental shift in how we structure and organize our code. Through this theoretical foundation, you will understand essential concepts for professional ML development: