Learnova
BESTSELLER

Machine Learning A–Z with Python

Regression, classification, clustering and more — hands-on

4.8(6,700 ratings)52,303 learners· 2h 3m
PNCreated by Priya Nair· Updated Oct 2026

What 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

1 · Foundations2 lessons · 20m
  • 1.2 The ML workflow11:25
2 · Supervised 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
3 · Unsupervised learning2 lessons · 24m
  • 3.1 K-means11:10
  • 3.2 PCA12:35
4 · In 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 · 106,003 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

AMArjun M.
Every algorithm explained with intuition first. Superb.