Build predictive models with scikit-learn and gain a practical understanding of the strengths and limitations of machine learning! Predictive modeling is a pillar of modern data science. In this field, scikit-learn is a central tool: it is easily accessible, yet powerful, and naturally dovetails in the 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 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.
The course is more than a cookbook: it will teach you to be critical about each step of the design of a predictive modeling pipeline: from choices in data preprocessing, to choosing models, gaining insights on their failure modes and interpreting their predictions.
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!
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
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.
Prerequisites
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 prior experience with the NumPy, pandas and Matplotlib libraries is recommended but not required
Course plan
Introduction
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
