Course presentation

Predictive modeling brings value to a vast variety of data, in business intelligence, health, industrial processes and scientific discoveries. It 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. 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.

Python

Introduction to numpy

Introduction to pandas

Introduction to Matplotlib

Course github repo

Course website