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Machine Learning Foundations in Python
Regression, classification, clustering and more — hands-on
4.8(61 ratings)298 learnersUpdated Oct 2026 · English · CC· 2h 3m
PNCreated by Priya Nair· Updated Oct 2026What you'll learn
- Build and evaluate ML models
- Choose the right algorithm
- Avoid overfitting
- Deploy a model as an API
The practical machine learning course: every major algorithm, when to use it, and how to evaluate it honestly, with scikit-learn and real datasets.
Course content
4 sections · 10 lessons · 2h 3m total length
01Foundations2 lessons · 20m
- 1.2 The ML workflow11:25
02Supervised learning4 lessons · 54m
- 2.1 Linear regression13:40
- 2.2 Logistic regression12:55
- 2.3 Decision trees and forests14:20
- 2.4 Gradient boosting13:05
03Unsupervised learning2 lessons · 24m
- 3.1 K-means11:10
- 3.2 PCA12:35
04In production2 lessons · 26m
- 4.1 Model evaluation13:50
- 4.2 Deploying a model12:15
Instructor
PN
Priya Nair
Data scientist and author of three Python books · 1,017 students · 6 courses
Priya spent ten years turning messy data into decisions at retail and health companies. She explains the why before the how, with real datasets in every lesson.
Learner reviews
The best explanation of this topic I have found.
Great teacher, great projects. Worth every minute.
The best explanation of this topic I have found.