Machine Learning Fundamentals

Interactive visuals and clear explanations. No coding required.

01

Pattern Discovery & Learning

Find the hidden pattern and learn how machines discover weights

02

Applications & Loss Functions

From real-world predictions to choosing the right error measure

03

Probability & Modern LLMs

From Monty Hall to chain-of-thought: understanding probability in AI

04

Classification: Making Decisions

From predicting numbers to predicting yes/no answers

05

Vectors & Deep Learning

From points to vectors: the foundation of modern AI

06

Matrices: The Building Blocks

From numbers to vectors to matrices—the foundation of neural networks

Foundation Complete
📚

Quick Recap: Chapters 1-6

Review your journey so far before diving into deep learning

✓ Pattern Discovery ✓ Vectors & Matrices ✓ Four Types of AI
Continue Your Journey
🚀

Deep Learning & Modern AI

Ready for neural networks, transformers, and production AI systems?

→ Embeddings & Attention → LLMs & RAG → AI Agents & Safety