Machine Learning with Python
- Free
- No certificate
- Beginner
- 20 hours
Machine Learning with Python, course code ML0101EN, introduces the main families of machine learning algorithms using Python and Jupyter notebooks. The Cognitive Class page states an estimated effort of 20 hours at beginner level, in English. Five modules run in order: supervised versus unsupervised learning and how machine learning relates to statistical modelling; supervised learning with K-nearest neighbours, decision trees and random forests; regression algorithms and model evaluation, including overfitting and underfitting; unsupervised learning with K-means, hierarchical and density-based clustering; and dimensionality reduction with collaborative filtering. The page lists Python for Data Science as a prerequisite and warns that the hands-on labs require working knowledge of Python for data analysis, suggesting the platform's Data Analysis with Python and Data Science Hands-on with Open Source Tools courses for anyone not yet comfortable in Jupyter.
Course staff include IBM data scientists and curriculum developers. The course is free of charge and the page says a certificate is offered on completion, provided at no cost. IBM also lists a Machine Learning with Python digital badge on its badges page, issued through Credly.
What you’ll learn
- Distinguish supervised from unsupervised learning and machine learning from statistical modelling
- Build classifiers with K-nearest neighbours, decision trees and random forests
- Fit regression models and evaluate them, recognising overfitting and underfitting
- Cluster data with K-means, hierarchical and density-based methods
- Reduce dimensionality and understand collaborative filtering
Who it’s for
Learners who can already write Python and load data with Pandas and want a survey of standard algorithms; not a maths-heavy course.
Syllabus
5 sections as listed on cognitiveclass.ai.
- Supervised vs Unsupervised Learning 4 lessons
- Supervised Learning I 5 lessons
- Supervised Learning II 4 lessons
- Unsupervised Learning 5 lessons
- Dimensionality Reduction & Collaborative Filtering 7 lessons
Course details
- Skills covered
- Machine Learning
- Python
- Includes
- 5 modules
- Prerequisites
- You have to do hands-on lab for this course. The tool that you use for hands-on is called Jupyter and it is one of the most popular tools used by data scientists. If you are not familiar with Jupyter, I would recommend that you take our free Data Science Hands-on with Open Source Tools., This…
On cognitiveclass.ai
Rated 4.7 out of 5 by 13,100 learners; 137,000 enrolled, as shown on the course page on 20 Oct 2026. These are cognitiveclass.ai’s own figures, not ours.
Source: IBM Cognitive Class (opens in a new tab) · Verified · Report a change
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