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Machine Learning Syllabus
Roadmap to Machine Learning. The complete Machine Learning outline — 10 subjects, 60 chapters and 207 topics — laid out so you can see exactly what has to be covered and tick each item off as you go.
Machine Learning subjects
Each subject below has its own page with the full chapter → topic → sub-topic tree and a set of real exam-style flashcards.
2 chapters · 8 topics
Mathematics5 chapters · 17 topics · 72 sub-topics
Python Programming4 chapters · 17 topics · 59 sub-topics
Core Concepts5 chapters · 12 topics · 45 sub-topics
Deep Learning and Neural Network6 chapters · 13 topics · 39 sub-topics
Natural Language Processing10 chapters · 51 topics
Machine Learning Tools and Libraries7 chapters · 43 topics
Project: Predicting House Prices7 chapters · 15 topics · 31 sub-topics
Project: Image Classification6 chapters · 13 topics · 27 sub-topics
Project: Recommendation System8 chapters · 18 topics · 42 sub-topics
Machine Learning flashcard decks
521 question-and-answer cards across 10 decks, written against this syllabus. Preview any deck free.
- Introduction to Machine Learning — 49 cards
- Mathematics — 59 cards
- Python Programming — 60 cards
- Core Concepts — 51 cards
- Deep Learning and Neural Network — 50 cards
- Natural Language Processing — 50 cards
- Machine Learning Tools and Libraries — 50 cards
- Project: Predicting House Prices — 50 cards
- Project: Image Classification — 50 cards
- Project: Recommendation System — 52 cards
How to work through the Machine Learning syllabus
The outline on this page is arranged the way the syllabus itself is: subject, then chapter, then topic, then sub-topic. That last level is what most study plans skip, and it is where coverage actually goes wrong — a chapter marked "done" often has three sub-topics inside it that were never touched.
Weight your plan accordingly: Natural Language Processing carries 51 topics while Introduction to Machine Learning carries 8. Giving both the same number of weeks is the most common planning mistake for Machine Learning.
The ~220 hour estimate above is a first pass only: roughly 45 minutes per topic plus 12 minutes per sub-topic. Revision cycles, past papers and mocks sit on top of that number.
Machine Learning syllabus FAQ
What subjects are in the Machine Learning syllabus?
Machine Learning covers 10 subjects: Introduction to Machine Learning, Mathematics, Python Programming, Core Concepts, Deep Learning and Neural Network, Natural Language Processing, Machine Learning Tools and Libraries and Project: Predicting House Prices, and 2 more. Together they contain 60 chapters and 207 named topics.
How big is the Machine Learning syllabus?
The full Machine Learning outline breaks down into 60 chapters, 207 topics and 315 sub-topics. Natural Language Processing is the largest single subject with 51 topics across 10 chapters.
How long does it take to cover the whole Machine Learning syllabus?
Roughly 220 hours for one full pass. That estimate allows about 45 minutes per topic plus 12 minutes per sub-topic, so it is first-pass coverage — revision, past papers and mock tests sit on top of it. At 3 hours a day that is about 10 weeks.
Are there Machine Learning flashcards?
Yes — 521 question-and-answer flashcards across 10 decks, one per subject, written against this syllabus. A sample of every deck is readable on this site for free, and the complete decks are in the Examius app.
Is this Machine Learning syllabus free to use?
Yes. Every page here is free to read, and importing the Machine Learning syllabus into the Examius app to tick off topics as you finish them is free too. No account is needed to start.
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