What You Need to Know Before
You Start

Starts 4 June 2025 13:49

Ends 4 June 2025

00 days
00 hours
00 minutes
00 seconds
course image

Machine learning in Python with scikit-learn

Machine Learning in Python with scikit-learn 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 mod.
via France Université Numerique

16 Courses


Not Specified

Optional upgrade avallable

All Levels

Progress at your own speed

Free

Optional upgrade avallable

Overview

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 free of charge, all the course materials are available at:

https:

//inria.github.io/scikit-learn-mooc/

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

Provider:

France Université Numérique

Categories:

Python Courses, Machine Learning Courses, scikit-learn Courses, Hyperparameter Tuning Courses


Subjects