A 22-module, beginner-friendly course: what AI, machine learning, deep learning and generative AI are; the statistics and probability you need; the Python you need; and hands-on scikit-learn projects in regression, classification, clustering, decision trees and feature engineering. Total running time: about 105 minutes. Each video has captions built in; watch the modules in order.
Watch the course
Open the full playlist on YouTube
Modules
| # | Module | Length | Who it’s for |
|---|---|---|---|
| 01 | AI-01 · AI, ML, Deep Learning, GenAI & Agents: The Big Picture | 4:36 | Anyone starting the AI & ML course; no maths or coding needed |
| 02 | AI-02 · How Machines Learn: y = f(x) | 4:07 | Beginners who completed AI-01 |
| 03 | AI-03 · Types of Machine Learning | 4:15 | Beginners who completed AI-02 |
| 04 | AI-04 · The ML Workflow: From Data to Deployment | 3:46 | Beginners who completed AI-03 |
| 05 | AI-05 · Neural Networks & Deep Learning Intuition | 4:57 | Beginners who completed AI-04 |
| 06 | AI-06 · Data, Analytics & Data Types | 5:02 | Beginners in AI and ML; no statistics or coding needed |
| 07 | AI-07 · Describing Data: Central Tendency & Spread | 5:08 | Beginners in the AI & ML track who completed AI-06 |
| 08 | AI-08 · Probability Essentials for ML | 5:22 | Beginners in the AI & ML track who completed AI-07 |
| 09 | AI-09 · Python Basics: Variables, Types & Input | 5:00 | Beginners in the AI & ML track; no programming experience needed |
| 10 | AI-10 · Python Collections: Lists, Tuples, Sets & Dictionaries | 4:42 | Beginners who completed AI-09 Python Basics |
| 11 | AI-11 · Writing Functions | 4:19 | Beginners who completed AI-10 Python Collections |
| 12 | AI-12 · Arguments, Lambdas, Filter & Map | 4:24 | Beginners who completed AI-11 Writing Functions |
| 13 | AI-13 · Classes & Objects: How scikit-learn Models Work | 3:59 | Beginners who completed AI-12; preparing for the hands-on machine learning modules |
| 14 | AI-14 · Linear Regression Explained | 4:39 | Learners who completed the ML workflow and statistics modules (AI-04 to AI-08) |
| 15 | AI-15 · Hands-on: Predicting Ice-Cream Demand with scikit-learn | 5:15 | Learners who completed AI-14 and the Python modules; first end-to-end model |
| 16 | AI-16 · Multiple Regression & Prediction Intervals | 4:21 | Learners who completed AI-14 and AI-15 |
| 17 | AI-17 · From Regression to Classification: Logistic Regression | 4:04 | Learners who completed AI-14 to AI-16 |
| 18 | AI-18 · Hands-on: Pass/Fail Classifier & Evaluation Metrics | 4:28 | Learners who completed AI-15 and AI-17 |
| 19 | AI-19 · Clustering & K-Means Concepts | 3:44 | Learners who completed the supervised-learning modules (AI-14 to AI-18) |
| 20 | AI-20 · Hands-on: Customer Segmentation with K-Means | 5:17 | Learners who completed AI-19 (clustering concepts) and the Python modules |
| 21 | AI-21 · Decision Trees, Overfitting & Random Forests | 6:15 | Learners who completed AI-15 to AI-20 |
| 22 | AI-22 · Feature Engineering & Data Cleaning | 7:12 | Learners who completed AI-15 to AI-21 |
Self-study handbooks
Each handbook follows the videos module by module, with screenshots, tips, practice exercises and a quiz with answers.