Build predictive models with scikit-learn and gain a practical understanding of the strengths and limitations of machine learning!
With this online course available 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.
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
Prerequisites
The requirements for this course are:
- Basic knowledge of Python programming : defining variables, writing functions, importing modules
- Some prior experience with the NumPy, pandas and Matplotlib libraries is recommended but not required.
For a quick introduction to these libraries, you can use the following resources: Introduction to NumPy and Matplotlib by Sebastian Raschka and 10 minutes to pandas.
Practical information:
- This online course is available for free on the FUN platform (Information & registration)
- All the materials will be made public and reusable
- Course dates : From May 18 to July 14, 2021
- Registration deadline : July 7, 2021
The MOOC is produced by Inria Learning Lab and runs on the FUN-mooc platform.
Follow course news on Twitter: @InriaLearnLab #ScikitLearnMooc