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CoursesArtificial Intelligence

Artificial Intelligence

beginner-advanced

Classical AI — the foundations of modern agents: search, knowledge representation, planning, and reinforcement learning.

10 modules83 lessons10 projects
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Never written code? You can still start here.

Module 0 opens with 18 lessons of programming from nothing: your first program, variables, loops, functions, reading errors, the terminal, Git and arrays, before any of the Artificial Intelligence material. No prior coding assumed.

What you'll learn

~121 h guided · about 25 weeks at 5 h/week · +128 h on projects

  • Explain why ChatGPT and Claude generate answers rather than looking them up like a search engine
  • Describe a program as ordinary text saved in a file and read by Python one line at a time
  • Explain how DQNs extend Q-learning to high-dimensional state spaces using neural function approximation
  • Adopt the "brief a colleague, don't query a search box" mental model
  • Run a Python file and produce output on screen using the print instruction
  • Implement a working DQN agent with experience replay and a target network in PyTorch
  • Identify why a briefed prompt reliably beats a lazy one
  • Use comments to leave notes that Python ignores when the program runs

Curriculum

0/83 lessons
1

AI Foundations — optional basics (free)

Week 0 · 20 lessons

0/20
1

Beyond the chatbox: what AI assistants really are

2

How the model "thinks" — and why it changes how you ask

3

The capability map: what AI does brilliantly

4

The limits: hallucination, freshness, and what never to trust it with

5

Your first real win: turn a 30-minute task into 3

6

Context is everything: give it what it needs to know

7

Give it a role and a goal

8

Constraints and format: define what "good" looks like

9

Show, don't tell: examples and templates

10

Iterate like a pro: steer, don't restart

11

Documents and reports: summarise, extract, and draft better

12

Spreadsheets and data: Excel formulas, analysis, and charts

13

Presentations: from blank page to a solid deck

14

Long, messy input: transcripts, threads, notes, and screenshots

15

ChatGPT, Claude, Copilot, Gemini: which to reach for, and when

16

Make smarter decisions: AI as your thinking partner

17

Chaining steps: multi-part tasks done reliably

18

Your prompt library: reusable templates that save hours

19

Staying safe: privacy, accuracy, and company data

20

Capstone: do a real task end-to-end (graded) → your certificate

2

Module 0 — Foundations: Logic, Probability & Algorithms for AI

Week 0 · 23 lessons

0/23
1

What a program is and how to run your first one

2

Storing values in variables, and why text and numbers behave differently

3

Making decisions with if, else and comparisons

4

Storing many values in a list

5

Looking things up by name with dictionaries

6

Doing something to every item with loops

7

Writing your own functions

8

Reading errors without panic

9

Finding the mistake in your own code

10

Saving your code in a file and running it

11

Importing code other people have written

12

Giving each project its own environment

13

Talking to your computer with the terminal

14

Saving your work with Git

15

Putting your work on GitHub

16

What an array is, and why a list is not enough

17

Picking out pieces: indexing and slicing, including two dimensions

18

Maths on a whole array at once, and where you go next

19

Logic & Sets for AI

20

Probability for AI

21

Graphs, Trees & Search Spaces

22

Algorithms & Complexity: BFS, DFS & Big-O

23

Python & Git for AI

3

AI Foundations

Week 1 · 5 lessons

0/5
1

History & Scope of AI

2

Search Problems & State Spaces

3

Breadth-First & Depth-First Search

4

Uninformed vs Informed Search

5

Problem Formulation

4

Heuristic Search

Week 2 · 5 lessons

0/5
1

A* Search Algorithm

2

Heuristic Design

3

Greedy Best-First Search

4

Local Search & Hill Climbing

5

Simulated Annealing

5

Adversarial Search

Week 3 · 5 lessons

0/5
1

Game Theory Basics

2

Minimax Algorithm

3

Alpha-Beta Pruning

4

Monte Carlo Tree Search

5

Game-Playing AI

6

Knowledge Representation

Week 4 · 5 lessons

0/5
1

Propositional Logic

2

First-Order Logic

3

Ontologies & Taxonomies

4

Knowledge Graphs

5

Reasoning Systems

7

Constraint Satisfaction

Week 5 · 5 lessons

0/5
1

CSP Formulation

2

Backtracking Search

3

Arc Consistency

4

Constraint Propagation

5

Real-World CSPs

8

Planning & Reasoning

Week 6 · 5 lessons

0/5
1

STRIPS Planning

2

PDDL Language

3

Classical Planning

4

Hierarchical Planning

5

Planning Under Uncertainty

9

Probabilistic AI

Week 7 · 5 lessons

0/5
1

Bayesian Networks

2

Probabilistic Inference

3

Hidden Markov Models

4

MCMC Methods

5

Decision Making Under Uncertainty

10

Reinforcement Learning

Week 8 · 5 lessons

0/5
1

RL Fundamentals

2

Q-Learning

3

Policy Gradients

4

Deep Q-Networks

5

RL Applications

Advanced tier

Advanced Artificial Intelligence

6 senior modules · 30lessons · capstone & certificate. Upgrade your plan to unlock the advanced tier.

Advanced Search & Optimisation

5 lessons

1

Simulated Annealing & Cooling Schedules

2

Genetic Algorithms & Evolutionary Strategies

3

Ant Colony & Swarm Intelligence

4

Constraint Satisfaction & Arc Consistency

5

Hybrid & Memetic Algorithms

Multi-Agent Systems

5 lessons

1

Agent Architectures & BDI Model

2

Communication & Coordination Protocols

3

Game-Theoretic Multi-Agent Interactions

4

Auction Mechanisms & Mechanism Design

5

Emergent Behaviour & Swarm Robotics

Advanced Probabilistic Reasoning

5 lessons

1

Hidden Markov Models & the Viterbi Algorithm

2

Kalman Filters & State Estimation

3

Particle Filters & Sequential Monte Carlo

4

Markov Decision Processes & Value Iteration

5

POMDPs & Belief-Space Planning

Deep Reinforcement Learning

5 lessons

1

Policy Gradient Methods & REINFORCE

2

Actor-Critic & Advantage Estimation

3

PPO & Trust Region Methods

4

SAC & Maximum Entropy RL

5

Model-Based RL & World Models

Neuro-Symbolic AI

5 lessons

1

Neural Network Refresher for Symbolic Integration

2

Differentiable Programming & Soft Logic

3

Knowledge Graph Embeddings & Reasoning

4

Neural Theorem Proving & Program Synthesis

5

Hybrid Architectures & Concept Learning

AI Safety & Alignment

5 lessons

1

Reward Hacking & Specification Gaming

2

Scalable Oversight & RLHF

3

Interpretability & Mechanistic Analysis

4

Robustness, Adversarial Attacks & Distributional Shift

5

Value Alignment & Responsible Deployment

What you'll build

10 projects · ~128 h · each graded against a rubric

  1. Pathfinding Visualiser

    beginner · ~8 h

    PythonPygameBFS/DFS/A*

  2. 8-Puzzle Solver

    beginner · ~8 h

    PythonHeuristic Search

  3. Tic-Tac-Toe AI

    beginner · ~6 h

    PythonMinimax

  4. Connect 4 with AI

    intermediate · ~12 h

    PythonAlpha-Beta Pruning

  5. Sudoku Solver & Generator

    intermediate · ~12 h

    PythonCSPBacktracking

  6. Knowledge Graph Builder

    intermediate · ~14 h

    PythonspaCyNetworkX

  7. Smart Calendar Scheduler

    intermediate · ~14 h

    PythonPlanningFlask

  8. Bayesian Network Analyser

    intermediate · ~12 h

    PythonpgmpyProbability

  9. Self-Driving Car Sim (2D)

    advanced · ~20 h

    PythonReinforcement LearningPygame

  10. AI Chess Engine

    advanced · ~22 h

    PythonMinimaxNeural Network

What we track

Everything below is graded by Nova and kept on your record: the thing an employer can open, run and verify.

Get started

Take the free assessment to see your skill level and get a personalised learning plan.

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Featured projects

Pathfinding Visualiser

PythonPygame

8-Puzzle Solver

PythonHeuristic Search

Tic-Tac-Toe AI

PythonMinimax

About

10 modules, 83 lessons
~121 hours of guided learning
10 portfolio projects (~128h)
AI-graded exercises

Artificial Intelligence

20 questions · ~30 minutes

Take the assessment →

The people behind this track

Nova grades every submission; these are the humans who wrote the brief and stand behind it.

Nikhil De Silva

Founder, Square1 Ai

Built Square1 Ai to close the gap between knowing and doing: every lesson ends in work that gets graded, and every student has someone in their corner.