Machine Learning
A beginner-to-intermediate tour of machine learning — what it is, the Python tools for it, and the core algorithms (regression, classification, clustering), taught with runnable scikit-learn code. For students comfortable reading basic Python.
Every lecture is published here first as a written article — the concepts to learn, runnable scikit-learn code, common mistakes, practice questions, and worked answers.
| Articles | Videos |
|---|---|
| 11 of 11 published | recording in progress |
Who This Course Is For
- Developers or students who can read and write basic Python (variables, functions, loops).
- No prior math or ML background assumed — the math/stats a given topic needs is introduced just-in-time, inside that topic's lecture.
What you need before Lecture 001: Python 3 installed, and (for the code examples from
Lecture 003 on) pip install numpy pandas scikit-learn.