AI-Era Skills
Machine Learning
Build, evaluate, and judge real models in Python, grounded in Raschka’s Python Machine Learning, Goodfellow’s Deep Learning, and Mitchell’s and Alpaydin’s ML texts: data preprocessing, regression, classification, clustering, association rule learning, reinforcement learning, NLP, deep learning, dimensionality reduction, and model selection.
Track curriculum
Data Preprocessing
8% of track · 4 sections
Regression
15% of track · 5 sections
Classification
15% of track · 5 sections
Clustering
8% of track · 3 sections
Association Rule Learning
6% of track · 2 sections
Reinforcement Learning
8% of track · 3 sections
Natural Language Processing
8% of track · 2 sections
Deep Learning
13% of track · 3 sections
Dimensionality Reduction
8% of track · 3 sections
Model Selection & Boosting
11% of track · 3 sections
Start studying
About the Machine Learning track
Hands-on machine learning in Python — from preprocessing to deep learning. Build, evaluate, and judge real models in Python, grounded in Raschka’s Python Machine Learning, Goodfellow’s Deep Learning, and Mitchell’s and Alpaydin’s ML texts: data preprocessing, regression, classification, clustering, association rule learning, reinforcement learning, NLP, deep learning, dimensionality reduction, and model selection.
AskBoudin covers all 10 topic areas with an AI tutor and unlimited AI-generated practice exercises. Start free — the first chapter is on us. Study at your own pace and check your mastery with full-length mock assessments.
Topics & weighting
- Data Preprocessing8%
- Regression15%
- Classification15%
- Clustering8%
- Association Rule Learning6%
- Reinforcement Learning8%
- Natural Language Processing8%
- Deep Learning13%
- Dimensionality Reduction8%
- Model Selection & Boosting11%
Machine Learning — Frequently asked questions
What does the Machine Learning track cover?
The track covers 10 topic areas across 33 lessons: Data Preprocessing, Regression, Classification, Clustering, Association Rule Learning, Reinforcement Learning, Natural Language Processing, Deep Learning, Dimensionality Reduction, Model Selection & Boosting. Every topic ends in a "Doing It With AI" arc — directing AI to do the work and verifying the result.
How much practice does the Machine Learning track include?
Around 330 practice exercises (roughly 4 hours 24 minutes of focused practice), plus AI-generated fresh questions on demand, so you never run out of material.
Who is the Machine Learning track for?
Product managers, marketing analysts, and product analysts who consume this kind of analysis — and increasingly generate it with AI — but don't do it professionally. No prior coding or statistics background is assumed.
Is the Machine Learning track free to start?
Yes. The first chapter is always free — study with the AI tutor and take practice questions before paying anything.
How does the AI tutor help with Machine Learning?
Ask the tutor any concept in plain English and get an instant explanation, then generate a fresh practice set on the same topic. It is available 24/7, so you are never stuck waiting for an instructor.
Aligned to the Molnar’s Interpretable ML, Bayesian Methods for Hackers, and Kroese et al.’s DSML · last reviewed 2026-07-08.