Learn AI Fundamentals

See how machines think and learn - from mazes and games to neural networks, rewards, image generators and ethics.

Start learning 18 lessons · about 6.5 hours · free
About this track

A beginner-friendly tour of artificial intelligence that you can touch. Drag vectors, fit lines by hand, tune a neuron’s weights, roll gradient descent down a loss curve, paint pixels, slide edge-detecting kernels over an image, and generate text one word at a time. Race search algorithms through a maze, lose at tic-tac-toe to an unbeatable AI, train a treasure-hunting robot, and drown a picture in noise the way image generators do.

Along the way you’ll learn what AI really is, the bit of math it runs on, how models learn from data and how we check they did, the classic ideas behind route planners and game engines, how AI learns from rewards and creates new things, and where AI goes wrong - bias, hallucinations and brittle predictions. Every lesson ends with small Python exercises that run in your browser, and the last one has you build a complete classifier from scratch.

Before you start
  • No AI or math background needed - every formula is explained.
  • Basic Python helps for the exercises; the lessons themselves need none.
  1. Unit 1 · 0/4 lessons

    Foundations

    What AI is, the math it runs on, how machines learn, and the three kinds of learning. Badge: AI curious

    1. 0x001What is artificial intelligence?Tell AI from ordinary software, see how AI, machine learning and deep learning nest, and meet the field’s ups and downs. 15 min 0/2 exercises solved 15 min 0/2 exercises solved
    2. 0x102The math toolkit: vectors and matricesDescribe things as lists of numbers, compare them with the dot product, and multiply matrices - the arithmetic AI runs on. 20 min 0/2 exercises solved 20 min 0/2 exercises solved
    3. 0x203How machines learn from dataFeatures and labels, the guess-check-adjust loop, train/test splits, overfitting, and why data quality decides everything. 20 min 0/2 exercises solved 20 min 0/2 exercises solved
    4. 0x304The three types of machine learningSupervised learning from answers, unsupervised learning from structure, and reinforcement learning from rewards. 20 min 0/2 exercises solved 20 min 0/2 exercises solved
  2. Unit 2 · 0/2 lessons

    First models

    Fit a line with linear regression and classify with k-nearest neighbors. Badge: Model maker

    1. 0x405Your first model: linear regressionFit a straight line to data, measure how wrong it is with mean squared error, and grow it to many features. 20 min 0/2 exercises solved 20 min 0/2 exercises solved
    2. 0x506Teaching a machine to classifyClassify new examples by asking their nearest neighbors, choose k, and see decision boundaries form. 20 min 0/2 exercises solved 20 min 0/2 exercises solved
  3. Unit 3 · 0/2 lessons

    Neural networks

    Build neurons and layers, and train them with gradient descent. Badge: Neuron tuner

    1. 0x607Neural networks: the basicsBuild an artificial neuron from a weighted sum and an activation, then stack neurons into layers. 22 min 0/2 exercises solved 22 min 0/2 exercises solved
    2. 0x708How networks learn: loss and gradient descentRoll downhill on the loss, pick a learning rate that neither crawls nor explodes, and see how backpropagation trains every weight. 22 min 0/2 exercises solved 22 min 0/2 exercises solved
  4. Unit 4 · 0/3 lessons

    Classic AI: search, games and probability

    Find paths with BFS and A*, play perfect games with minimax, and reason under uncertainty with Bayes’ rule. Badge: Puzzle solver

    1. 0x809Search: how AI finds its wayRace breadth-first, depth-first, greedy and A* search through a maze, and see why a good guess saves a lot of work. 24 min 0/2 exercises solved 24 min 0/2 exercises solved
    2. 0x9010Game-playing AI: minimaxPlay an unbeatable tic-tac-toe AI, peek at how it scores every move, and see how alpha-beta pruning skips most of the work. 22 min 0/2 exercises solved 22 min 0/2 exercises solved
    3. 0xA011Reasoning with uncertainty: Bayes’ ruleDiscover why a positive test can still mean you’re probably fine, update beliefs as evidence arrives, and build a tiny spam filter. 22 min 0/2 exercises solved 22 min 0/2 exercises solved
  5. Unit 5 · 0/3 lessons

    Rewards, creativity and report cards

    Train agents with rewards, see how generative AI creates images, and measure models honestly. Badge: Reward hunter

    1. 0xB012Reinforcement learning: learning by trial and errorTrain a treasure-hunting robot with Q-learning, tune its curiosity and patience, and meet agents that cheat their own reward. 25 min 0/2 exercises solved 25 min 0/2 exercises solved
    2. 0xC013Generative AI: how machines createFrom telling things apart to making new ones: autoregressive text, GANs, latent spaces, and diffusion models that paint by removing noise. 24 min 0/2 exercises solved 24 min 0/2 exercises solved
    3. 0xD014Report cards for AI: measuring modelsWhy 99% accuracy can be useless, how a confusion matrix exposes a model’s mistakes, and how to trade precision against recall. 22 min 0/2 exercises solved 22 min 0/2 exercises solved
  6. Unit 6 · 0/4 lessons

    AI in the world

    Computer vision, language models, ethics and limitations - and where to go next. Badge: AI explorer

    1. 0xE015Computer vision: how machines seeTreat images as grids of numbers, slide kernels across them to find edges, and stack convolutions into a CNN. 22 min 0/2 exercises solved 22 min 0/2 exercises solved
    2. 0xF016Language and large language modelsSplit text into tokens, give words meaning with embeddings, and generate text one predicted token at a time. 22 min 0/2 exercises solved 22 min 0/2 exercises solved
    3. 0x10017AI ethics, bias and limitationsFind where bias sneaks in, recognize what AI can’t do, and check a system’s errors group by group. 20 min 0/2 exercises solved 20 min 0/2 exercises solved
    4. 0x11018Where to go nextReview the core vocabulary, build a complete tiny classifier end to end, and pick your next track. 15 min 0/1 exercises solved 15 min 0/1 exercises solved
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