Machine Learning Curriculum

Technology

A structured, level-by-level path through Machine Learning. Start with Foundations of Supervised learning and Core methods in Model evaluation at the Intermediate level, then build toward advanced practice and expert-level mastery — with hands-on projects and quizzes at every stage.

3 levels 9 modules 27 topics 6 weeks · about 24 hours · 8 weeks · about 32 hours · 10 weeks · about 40 hours
Intermediate

Intermediate Machine Learning

6 weeks · about 24 hours

3 modules

Advanced

Advanced Machine Learning

8 weeks · about 32 hours

3 modules

Expert

Expert Machine Learning

10 weeks · about 40 hours

3 modules

Machine Learning Course Curriculum

9 modules across 3 levels.

Intermediate

Intermediate Machine Learning

Start with Supervised learning, Model evaluation and Feature engineering. Each module explains the idea, shows a worked example and ends with a short project, so Machine Learning becomes something you can do rather than something you have only read about.

6 weeks · about 24 hours 3 modules

Topics

Project

Write a one-page explanation of Supervised learning in your own words, with an example you chose yourself, and test it against the module checklist.

Learning Path

1

Foundations of Supervised learning

Work through the three topics in this module, complete the project, then answer the self-check questions before moving on.

2

Core methods in Model evaluation

Work through the three topics in this module, complete the project, then answer the self-check questions before moving on.

Prerequisites: Foundations of Supervised learning

3

Applying Feature engineering

Work through the three topics in this module, complete the project, then answer the self-check questions before moving on.

Prerequisites: Core methods in Model evaluation

Practice Problems

EasyExplain Supervised learning to someone who has never studied the subject, using one worked example.
Solution: A strong answer defines supervised learning precisely, gives one concrete example, and names the limitation it runs into.
MediumExplain Model evaluation to someone who has never studied the subject, using one worked example.
Solution: A strong answer defines model evaluation precisely, gives one concrete example, and names the limitation it runs into.
HardExplain Feature engineering to someone who has never studied the subject, using one worked example.
Solution: A strong answer defines feature engineering precisely, gives one concrete example, and names the limitation it runs into.

Glossary

Supervised learning

A central idea in the intermediate Machine Learning path, covered in the module of the same name.

Example: apply Supervised learning to a small, familiar case step by step, then check your reasoning against the walkthrough.

Model evaluation

A central idea in the intermediate Machine Learning path, covered in the module of the same name.

Example: apply Model evaluation to a small, familiar case step by step, then check your reasoning against the walkthrough.

Feature engineering

A central idea in the intermediate Machine Learning path, covered in the module of the same name.

Example: apply Feature engineering to a small, familiar case step by step, then check your reasoning against the walkthrough.

About the Machine Learning curriculum

The Machine Learning curriculum is a complete, self-paced course in technology, organised into 3 progressive levels and 9 modules. It opens with Foundations of Supervised learning and Core methods in Model evaluation, moves through Applying Feature engineering and finishes with expert-level work on Leading practice in Deployment & monitoring and Mastering Fairness & model risk.

Across the whole path you work through 27 individual topics — from Supervised learning and Hands-on: supervised learning at the start to Hands-on: fairness & model risk and Checking your understanding of fairness & model risk by the end. The levels are estimated at 6 weeks · about 24 hours, 8 weeks · about 32 hours and 10 weeks · about 40 hours. Each level assumes the one before it, so you can work straight through from Intermediate to Expert, or start at the level that matches what you already know.

It is written for developers, engineers and career-switchers who want one structured route instead of scattered tutorials. Because the work is judged by what you can build, the path leans on projects: each level ends with a brief you complete yourself, then compare against the guided walkthrough.

Every module pairs a written summary with a worked example, a short quiz to check the idea landed, and a project brief drawn from real practice. You are never asked to take a concept on trust: the reasoning, the common mistakes and the situations where the approach stops working are all laid out, so you finish a module able to explain the idea rather than only recognise it.

What you’ll be able to do

By the end of the Machine Learning path you should be able to work confidently with:

Supervised learning

Hands-on: supervised learning

Common pitfalls in supervised learning

Model evaluation

Putting model evaluation to work

Common pitfalls in model evaluation

Feature engineering

Hands-on: feature engineering

How the Machine Learning curriculum is structured

Intermediate 6 weeks · about 24 hours 3 modules 9 topics

Intermediate Machine Learning

Start with Supervised learning, Model evaluation and Feature engineering. Each module explains the idea, shows a worked example and ends with a short project, so Machine Learning becomes something you can do rather than something you have only read about.

Modules: Foundations of Supervised learning · Core methods in Model evaluation · Applying Feature engineering

  • Supervised learning: Supervised learning is introduced from first principles, with the few terms you actually need and an example that shows how the idea behaves in practice.
  • Hands-on: supervised learning: Supervised learning is where many Machine Learning learners first get stuck, so the module slows down, isolates the difficult step and rebuilds it with you.
  • Common pitfalls in supervised learning: Supervised learning is introduced from first principles, with the few terms you actually need and an example that shows how the idea behaves in practice.

Project: Write a one-page explanation of Supervised learning in your own words, with an example you chose yourself, and test it against the module checklist.

Advanced 8 weeks · about 32 hours 3 modules 9 topics

Advanced Machine Learning

Build on the intermediate level through Bias, variance & regularisation, Trees & ensembles and Unsupervised learning. The emphasis shifts from knowing the ideas to choosing between them — trade-offs, failure modes and the reasoning behind each decision.

Modules: In depth Bias, variance & regularisation · Patterns and trade-offs in Trees & ensembles · Scaling Unsupervised learning

  • Bias, variance & regularisation: Bias, variance & regularisation is pushed past the textbook case: competing approaches, their trade-offs and the situations where each one breaks down.
  • Putting bias, variance & regularisation to work: The module examines Bias, variance & regularisation closely, including the assumptions beneath it and what changes when those assumptions stop holding.
  • Common pitfalls in bias, variance & regularisation: Bias, variance & regularisation is pushed past the textbook case: competing approaches, their trade-offs and the situations where each one breaks down.

Project: Produce a short case analysis in which Bias, variance & regularisation drives the recommendation, including the alternatives you rejected and why.

Expert 10 weeks · about 40 hours 3 modules 9 topics

Expert Machine Learning

Work at the edge of Machine Learning through Neural networks, Deployment & monitoring and Fairness & model risk. The modules assume fluency and ask for judgement: designing, critiquing and defending work at a professional standard.

Modules: Frontiers of Neural networks · Leading practice in Deployment & monitoring · Mastering Fairness & model risk

  • Neural networks: This module asks you to critique and extend Neural networks rather than simply apply it, producing work another expert could review.
  • Hands-on: neural networks: At expert level Neural networks is treated as a field of judgement: current practice, contested positions and the evidence behind each.
  • Checking your understanding of neural networks: This module asks you to critique and extend Neural networks rather than simply apply it, producing work another expert could review.

Project: Write a professional-standard brief on Neural networks — argument, evidence, limitations — plus a plan for taking the work further.

How to study Machine Learning, phase by phase

  1. 1

    Phase 1 — Intermediate

    Set aside around 6 weeks · about 24 hours. Work through 3 modules, starting with Foundations of Supervised learning and Core methods in Model evaluation. Finish the phase by completing the project brief: Write a one-page explanation of Supervised learning in your own words, with an example you chose yourself, and test it against the module checklist.

  2. 2

    Phase 2 — Advanced

    Set aside around 8 weeks · about 32 hours. Work through 3 modules, starting with In depth Bias, variance & regularisation and Patterns and trade-offs in Trees & ensembles. Finish the phase by completing the project brief: Produce a short case analysis in which Bias, variance & regularisation drives the recommendation, including the alternatives you rejected and why.

  3. 3

    Phase 3 — Expert

    Set aside around 10 weeks · about 40 hours. Work through 3 modules, starting with Frontiers of Neural networks and Leading practice in Deployment & monitoring. Finish the phase by completing the project brief: Write a professional-standard brief on Neural networks — argument, evidence, limitations — plus a plan for taking the work further.

  4. 4

    Phase 4 — Consolidate

    Return to the sections that felt weakest, redo their quizzes from memory, and rebuild the Write a one-page explanation of Supervised learning in your own words, with an example you chose yourself, and test it against the module checklist. without following along. Explaining Machine Learning to someone else in plain language is the clearest signal that the Expert level has landed.

Once you have finished, the Machine Learning path is a reasonable foundation for software engineering, technical consulting and product roles. Because the curriculum is generated and kept as a structured outline rather than fixed videos, you can revisit any module, regenerate the outline at a different depth, or save it to your library and work through it over weeks rather than in one sitting.

Machine Learning — Frequently Asked Questions

Common questions about studying Machine Learning on LearnAnyCourse.

What does the Machine Learning curriculum cover?

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The Machine Learning curriculum is organised into 3 levels (Intermediate, Advanced and Expert) and 9 modules, including Foundations of Supervised learning, Core methods in Model evaluation, Applying Feature engineering, In depth Bias, variance & regularisation, Patterns and trade-offs in Trees & ensembles and Scaling Unsupervised learning.

How long does it take to learn Machine Learning?

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Each level is designed to take about 6 weeks · about 24 hours, 8 weeks · about 32 hours and 10 weeks · about 40 hours. Most learners work through one level at a time at their own pace.

Is the Machine Learning curriculum free?

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Yes. Every Machine Learning curriculum on LearnAnyCourse is free to read and study — create an account only if you want to save curricula to your personal library.

Do I need prior experience to study Machine Learning?

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The curriculum starts at the intermediate level and builds upward, so you can begin with the fundamentals and progress to expert-level Machine Learning topics without jumping ahead.

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