Build predictive models with scikit-learn and gain a practical understanding of the strengths and limitations of machine learning!
With this online course available for free in English, you will learn the basics of machine learning and how to use the scikit-learn Python library. This Mooc is accessible to anybody with basic Python programming skills.
The training, developed by the scikit-learn team, is mainly practical, focusing on application examples, and based on Python code executed by the participants. Everything is integrated in the Mooc and you don’t have to install anything.
Step-by-step and didactic lessons introduce the fundamental methodological and software tools of machine learning, and is as such a stepping stone to more advanced challenges in artificial intelligence, text mining, or data science.
What you will learn
At the end of this course, you will be able to:
- 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
Course curriculum
- Introduction: Machine Learning concepts
- Module 1: The Predictive Modeling Pipeline
- Module 2: Selecting the best model
- Module 3: Hyperparameter tuning
- Module 4: Linear Models
- Module 5: Decision tree models
- Module 6: Ensemble of models
- Module 7: Evaluating model performance
Who’s this course for?
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.
Course format
The course will cover practical aspects through the use of Jupyter notebooks and regular exercises. Throughout the course, best scikit-learn best practices will be highlighted. As well as practical aspects, intuitions will be provided, in order to use scikit-learn in a methodologically sound way.
Useful information and links:
- Course dates
- session 4 in self-paced: from 8 November 2023 to 7 November 2024
- session 3: from October 18, 2022 to January 17, 2023 – Registration until January 10, 2023
- session 2: from February 15 to May 16, 2022 – Registration deadline May 8, 2022
- session 1: from May 18 to July 14, 2021
- Information and registration on Fun-Mooc platform
- Twitter: #ScikitLearnMooc
