Machine learning in Python with scikit-learn

Description

Predictive modeling is a pillar of modern data science. In this field, scikit-learn is a central tool: it is easily accessible and yet powerful, and it dovetails in a wider ecosystem of data-science tools based on the Python programming language.

This course is an in-depth introduction to predictive modeling with scikit-learn. Step-by-step and didactic lessons will give you the fundamental tools and approaches of machine learning, and is as such a stepping stone to more advanced challenges in artificial intelligence, text mining, or data science.

The course covers the software tools to build and evaluate predictive pipelines, as well as the related concepts and statistical intuitions. It is more than a cookbook: it will teach you to understand and be critical about each step, from choosing models to interpreting them.

The training will be essentially practical, focusing on examples of applications with code executed by the participants.

The MOOC is free of charge, all the course materials are available at:https://inria.github.io/scikit-learn-mooc/

The authors of the course are scikit-learn core developers, they will be your guides throughout the training!

Plan de cours

• Introduction: Machine Learning concepts
• Module 1. The Predictive Modeling Pipeline
• Module 2. Selecting the best model
• Module 3. Hyperparameters tuning
• Module 4. Linear Models
• Module 5. Decision tree models
• Module 6. Ensemble of models
• Module 7. Evaluating model performance