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Volume 3

Volume 3 - Machine Learning Engineering

Volume 2 taught the mathematics. This volume spends it. Every chapter takes one classical machine learning algorithm, builds it from scratch using only the math Volume 2 already covered, verifies it against a real dataset, and then shows exactly where that algorithm shows up — or breaks down — in a production system.

Chapters

19

Lesson Reading

8 hr 45 min

Labs

19

Interview Sets

19

Begin First ChapterAcademy PassBack to Full Roadmap

What This Volume Covers

Read the sections in order if you are building from scratch. Experienced readers can jump to a section, but the chapters are sequenced to build vocabulary, mental models, implementation judgment, and architecture readiness.

Section 1

Supervised Learning

6 chapters · 2 hr 50 min lesson reading

Section Overview
  1. Chapter 1The Supervised Learning Workflow40 minutesAcademy Pass
  2. Chapter 2Linear Regression25 minutesAcademy Pass
  3. Chapter 3Logistic Regression25 minutesAcademy Pass
  4. Chapter 4Decision Trees25 minutesAcademy Pass
  5. Chapter 5Ensemble Methods25 minutesAcademy Pass
  6. Chapter 6Model Evaluation & the Bias-Variance Tradeoff30 minutesAcademy Pass

Section 2

Unsupervised Learning & Dimensionality Reduction

6 chapters · 2 hr 20 min lesson reading

Section Overview
  1. Chapter 7Clustering: K-Means from Scratch25 minutesAcademy Pass
  2. Chapter 8Hierarchical & Density-Based Clustering20 minutesAcademy Pass
  3. Chapter 9Principal Component Analysis25 minutesAcademy Pass
  4. Chapter 10Anomaly Detection25 minutesAcademy Pass
  5. Chapter 11Recommender Systems20 minutesAcademy Pass
  6. Chapter 12Feature Engineering & Data Preprocessing25 minutesAcademy Pass

Section 3

ML Engineering in Practice

7 chapters · 3 hr 35 min lesson reading

Section Overview
  1. Chapter 13Regularization25 minutesAcademy Pass
  2. Chapter 14Hyperparameter Tuning & Cross-Validation25 minutesAcademy Pass
  3. Chapter 15Handling Imbalanced & Messy Real-World Data25 minutesAcademy Pass
  4. Chapter 16Model Interpretability35 minutesAcademy Pass
  5. Chapter 17ML Pipelines in Production35 minutesAcademy Pass
  6. Chapter 18Where Classical ML Ends and Deep Learning Begins35 minutesAcademy Pass
  7. Chapter 19End of Volume Review & Interview Questions35 minutesAcademy Pass