Artificial Intelligence Curriculum

Technology

A structured, level-by-level path through Artificial Intelligence. Start with Foundations of Search & planning and Core methods in Knowledge representation 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 Artificial Intelligence

6 weeks · about 24 hours

3 modules

Advanced

Advanced Artificial Intelligence

8 weeks · about 32 hours

3 modules

Expert

Expert Artificial Intelligence

10 weeks · about 40 hours

3 modules

Artificial Intelligence Course Curriculum

9 modules across 3 levels.

Intermediate

Intermediate Artificial Intelligence

Start with Search & planning, Knowledge representation and Probabilistic reasoning. Each module explains the idea, shows a worked example and ends with a short project, so Artificial Intelligence 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 Search & planning in your own words, with an example you chose yourself, and test it against the module checklist.

Learning Path

1

Foundations of Search & planning

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

2

Core methods in Knowledge representation

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

Prerequisites: Foundations of Search & planning

3

Applying Probabilistic reasoning

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

Prerequisites: Core methods in Knowledge representation

Practice Problems

EasyExplain Search & planning to someone who has never studied the subject, using one worked example.
Solution: A strong answer defines search & planning precisely, gives one concrete example, and names the limitation it runs into.
MediumExplain Knowledge representation to someone who has never studied the subject, using one worked example.
Solution: A strong answer defines knowledge representation precisely, gives one concrete example, and names the limitation it runs into.
HardExplain Probabilistic reasoning to someone who has never studied the subject, using one worked example.
Solution: A strong answer defines probabilistic reasoning precisely, gives one concrete example, and names the limitation it runs into.

Glossary

Search & planning

A central idea in the intermediate Artificial Intelligence path, covered in the module of the same name.

Example: apply Search & planning to a small, familiar case step by step, then check your reasoning against the walkthrough.

Knowledge representation

A central idea in the intermediate Artificial Intelligence path, covered in the module of the same name.

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

Probabilistic reasoning

A central idea in the intermediate Artificial Intelligence path, covered in the module of the same name.

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

About the Artificial Intelligence curriculum

The Artificial Intelligence curriculum is a complete, self-paced course in technology, organised into 3 progressive levels and 9 modules. It opens with Foundations of Search & planning and Core methods in Knowledge representation, moves through Applying Probabilistic reasoning and finishes with expert-level work on Leading practice in Generative models and Mastering AI safety & governance.

Across the whole path you work through 27 individual topics — from Search & planning and Hands-on: search & planning at the start to Putting ai safety & governance to work and Checking your understanding of ai safety & governance 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 Artificial Intelligence path you should be able to work confidently with:

Search & planning

Hands-on: search & planning

Common pitfalls in search & planning

Knowledge representation

Putting knowledge representation to work

Common pitfalls in knowledge representation

Probabilistic reasoning

Hands-on: probabilistic reasoning

How the Artificial Intelligence curriculum is structured

Intermediate 6 weeks · about 24 hours 3 modules 9 topics

Intermediate Artificial Intelligence

Start with Search & planning, Knowledge representation and Probabilistic reasoning. Each module explains the idea, shows a worked example and ends with a short project, so Artificial Intelligence becomes something you can do rather than something you have only read about.

Modules: Foundations of Search & planning · Core methods in Knowledge representation · Applying Probabilistic reasoning

  • Search & planning: Search & planning 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: search & planning: Search & planning is where many Artificial Intelligence learners first get stuck, so the module slows down, isolates the difficult step and rebuilds it with you.
  • Common pitfalls in search & planning: Search & planning 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 Search & planning 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 Artificial Intelligence

Build on the intermediate level through Machine learning foundations, Natural language processing and Computer vision. The emphasis shifts from knowing the ideas to choosing between them — trade-offs, failure modes and the reasoning behind each decision.

Modules: In depth Machine learning foundations · Patterns and trade-offs in Natural language processing · Scaling Computer vision

  • Machine learning foundations: Machine learning foundations is pushed past the textbook case: competing approaches, their trade-offs and the situations where each one breaks down.
  • Putting machine learning foundations to work: The module examines Machine learning foundations closely, including the assumptions beneath it and what changes when those assumptions stop holding.
  • Common pitfalls in machine learning foundations: Machine learning foundations 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 Machine learning foundations drives the recommendation, including the alternatives you rejected and why.

Expert 10 weeks · about 40 hours 3 modules 9 topics

Expert Artificial Intelligence

Work at the edge of Artificial Intelligence through Reinforcement learning, Generative models and AI safety & governance. The modules assume fluency and ask for judgement: designing, critiquing and defending work at a professional standard.

Modules: Frontiers of Reinforcement learning · Leading practice in Generative models · Mastering AI safety & governance

  • Reinforcement learning: Reinforcement learning is examined at the frontier, with an emphasis on open problems, rigorous argument and decisions you can defend.
  • Putting reinforcement learning to work: This module asks you to critique and extend Reinforcement learning rather than simply apply it, producing work another expert could review.
  • Checking your understanding of reinforcement learning: Reinforcement learning is examined at the frontier, with an emphasis on open problems, rigorous argument and decisions you can defend.

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

How to study Artificial Intelligence, phase by phase

  1. 1

    Phase 1 — Intermediate

    Set aside around 6 weeks · about 24 hours. Work through 3 modules, starting with Foundations of Search & planning and Core methods in Knowledge representation. Finish the phase by completing the project brief: Write a one-page explanation of Search & planning 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 Machine learning foundations and Patterns and trade-offs in Natural language processing. Finish the phase by completing the project brief: Produce a short case analysis in which Machine learning foundations 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 Reinforcement learning and Leading practice in Generative models. Finish the phase by completing the project brief: Write a professional-standard brief on Reinforcement learning — 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 Search & planning in your own words, with an example you chose yourself, and test it against the module checklist. without following along. Explaining Artificial Intelligence to someone else in plain language is the clearest signal that the Expert level has landed.

Once you have finished, the Artificial Intelligence 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.

Artificial Intelligence — Frequently Asked Questions

Common questions about studying Artificial Intelligence on LearnAnyCourse.

What does the Artificial Intelligence curriculum cover?

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The Artificial Intelligence curriculum is organised into 3 levels (Intermediate, Advanced and Expert) and 9 modules, including Foundations of Search & planning, Core methods in Knowledge representation, Applying Probabilistic reasoning, In depth Machine learning foundations, Patterns and trade-offs in Natural language processing and Scaling Computer vision.

How long does it take to learn Artificial Intelligence?

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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 Artificial Intelligence curriculum free?

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Yes. Every Artificial Intelligence 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 Artificial Intelligence?

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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 Artificial Intelligence topics without jumping ahead.

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