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The ML / AI track.

Machine learning, deep learning, neural networks, scikit-learn, TensorFlow and PyTorch. Built as a curriculum — 22 chapters, recommended spine if you're new, side-quests when you're ready.

Continue from Introduction to Machine Learning →
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CURRICULUM

22 chapters · 198 topics

01

ML Basics

SPINEBeginner
26 topics · ~3h 2m · 0/26 read
02

Math for ML

SPINEIntermediate
7 topics · ~49m · 0/7 read
03

Algorithms

SPINEIntermediate
21 topics · ~2h 27m · 0/21 read
04

Deep Learning

SPINEAdvanced
23 topics · ~2h 41m · 0/23 read
05

Reinforcement Learning

SPINEAdvanced
12 topics · ~1h 24m · 0/12 read
06

From Scratch

SPINEAdvanced
4 topics · ~28m · 0/4 read
07

Tools

SPINEIntermediate
12 topics · ~1h 24m · 0/12 read
08

NLP

SPINEIntermediate
11 topics · ~1h 17m · 0/11 read
09

MLOps

SPINEAdvanced
14 topics · ~1h 38m · 0/14 read
10

TensorFlow & Keras

SPINEIntermediate
10 topics · ~1h 10m · 0/10 read
11

Scikit-Learn

SPINEBeginner
8 topics · ~56m · 0/8 read
12

PyTorch

SPINEIntermediate
7 topics · ~49m · 0/7 read
13

SPINEIntermediate
3 topics · ~21m · 0/3 read
14

Prompt Engineering

Beginner
5 topics · ~35m · 0/5 read
15

LLM Basics

Advanced
8 topics · ~56m · 0/8 read
16

LLM APIs

Intermediate
3 topics · ~21m · 0/3 read
17

RAG

Intermediate
5 topics · ~35m · 0/5 read
18

AI Agents

Intermediate
5 topics · ~35m · 0/5 read
19

Agent Frameworks

Intermediate
4 topics · ~28m · 0/4 read
20

Multi-Agent

Advanced
3 topics · ~21m · 0/3 read
21

Context Engineering

Intermediate
4 topics · ~28m · 0/4 read
22

Observability

Intermediate
3 topics · ~21m · 0/3 read
START HERE · CHAPTER 01

ML Basics

26 topics · Begin with Introduction to Machine Learning

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