Machine learning in Python with scikit-learn

Réf. 41026

Durée : 13 semaines
Effort : 36 heures
Rythme: ~2h45/semaine

Build predictive models with scikit-learn and gain a practical understanding of the strengths and limitations of machine learning!

Ce que vous allez apprendre

À la fin de ce cours, vous saurez :

Grasp the fundamental concepts of machine learning
Build a predictive modeling pipeline with scikit-learn
Develop intuitions behind machine learning models from linear models to gradient-boosted decision trees
Evaluate the statistical performance of your models

Please note that this course is available in english only.

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 completely free of charge. All the course materials are also available on a github repository.

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

Format

The course will cover practical aspects through the use of Jupyter notebooks and regular exercises. Throughout the course, we will highligh scikit-learn best practices and give you the intuition to use scikit-learn in a methodologically sound way.

Prérequis

The course aims to be accessible without a strong technical background. The requirements for this course are:

- basic knowledge of Python programming : defining variables, writing functions, importing modules
- some practical experience of the NumPy, Pandas and Matplotlib libraries is useful but not required. For a basic knowledge of these libraries, you can use the following resources : Introduction to NumPy and Matplotlib by Sebastian Raschka and 10 minutes to pandas.

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

Autres sessions

Archivé

Du 18 mai 2021 au 14 juillet 2021
Du 15 février 2022 au 17 mai 2022

Équipe pédagogique

Licence

Licence pour le contenu du cours

Attribution

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Adapter — remixer, transformer et créer à partir du matériel pour toute utilisation, y compris commerciale.

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Attribution — Vous devez créditer l'oeuvre, intégrer un lien vers la licence et indiquer si des modifications ont été effectuées à l'oeuvre. Vous devez indiquer ces informations par tous les moyens raisonnables, sans toutefois suggérer que l'offrant vous soutient ou soutient la façon dont vous avez utilisé son oeuvre.

Licence pour le contenu créé par les participants du cours

Attribution - Pas d’Utilisation Commerciale - Pas de Modification