See how machines learn

Free interactive AI course: 34 lessons and 25 hands-on labs on machine learning, neural networks, LLMs, computer vision and more. Real algorithms run in…

Lessons

• AI Fundamentals

  What AI is, where it came from, and how today’s systems actually work under the hood.

• Machine Learning

  How computers learn patterns from examples instead of following hand-written rules.

• Evaluating Models

  Accuracy lies. Learn the metrics and habits that tell you whether a model really works.

• Neural Networks and Deep Learning

  From a single neuron to deep networks: activations, backpropagation and gradient descent.

• Unsupervised Learning

  Finding structure in data nobody labelled: clustering, density and dimensionality reduction.

• AI Ethics and Safety

  Bias, fairness, transparency and alignment, measured on real trade-offs rather than slogans.

• Computer Vision

  How machines turn pixels into meaning, from edge filters to convolutional networks.

• Object Detection

  Finding and labelling every object in a scene, from R-CNN to YOLO.

• Image Segmentation

  Labelling images pixel by pixel: semantic, instance and panoptic segmentation.

• Natural Language Processing

  Teaching machines to read: tokens, embeddings, sentiment and the road to language models.

• Machine Translation

  How translation went from phrase tables to neural encoder-decoder models.

• Transformers and Attention

  The architecture behind modern AI: self-attention, multi-head attention and positional encoding.

• Generative AI

  How machines learn to create text, images, audio and video: GANs, VAEs, autoregressive models and diffusion.

• Search and Optimization

  How AI plans: graph search, heuristics, A* and local search for problems too big to brute-force.

• Reinforcement Learning

  Learning by trial and error: rewards, policies, Q-learning and the exploration trade-off.

• Large Language Models

  How models like Claude and GPT are built: pretraining, scaling laws, fine-tuning, RLHF and reasoning.

• Embeddings and Retrieval

  Turning meaning into vectors, searching by similarity, and grounding LLM answers in your own documents.

• AI Agents and Tool Use

  How language models plan, call tools and act in loops, and why reliability is the hard part.

• Decision Models and Jev

  Why a model that answers with a calibrated choice instead of text is one of 2026’s biggest stories.

• Multimodal AI

  Models that see, read and listen at once: CLIP, zero-shot classification and vision-language models.

• The Maths Behind ML

  The handful of ideas from linear algebra, calculus and probability that every model is built on, made visual.

• Forests and Boosting

  Why many weak trees beat one strong one, and why gradient boosting still wins on spreadsheets.

• Recommender Systems

  How Netflix, Spotify and TikTok decide what you see next, from collaborative filtering to two-tower models.

• Robots and World Models

  How AI leaves the screen: perception, control, learning from demonstration and the vision-language-action models of 2025-26.

• Speech and Audio AI

  How machines hear and speak: spectrograms, speech recognition, voice synthesis and real-time voice assistants.

• Prompt and Context Engineering

  Getting reliable work out of language models: instructions, examples, structure, context and evaluation.

• Fine-Tuning and Efficient Models

  Adapting and shrinking big models: fine-tuning, LoRA, quantization and distillation.

• Interpretability

  Looking inside neural networks: features, circuits, probes and sparse autoencoders.

• Opus 5.5, Fable 5.1 and Looped Transformers

  How a cheaper model can match a bigger one: extra depth by looping layers versus extra size, and what is actually known about Claude Opus 5.5 and Fable 5.1.

• Tokenization

  How language models chop text into tokens, why it explains their strangest mistakes, and what it costs you per word.

• Scaling Laws and Pretraining

  Why bigger models trained on more data get predictably better, how labs plan billion-dollar training runs, and where the curves bend.

• Post-Training and Alignment

  How a raw text predictor becomes a helpful assistant: instruction tuning, RLHF, DPO, constitutional AI and rewards you can verify.

• Reasoning Models

  Why models that think before answering got so much better at maths and code: chain of thought, test-time compute and learning from verifiable rewards.

• Mixture of Experts

  How frontier models get huge without getting slow: routing each token to a few specialist sub-networks.

Labs

• Neural Network Playground

  Build a network layer by layer and watch it learn a decision boundary in real time.

• Gradient Descent Lab

  Drop optimizers onto a loss landscape and race SGD, momentum, RMSProp and Adam to the minimum.

• Decision Tree Builder

  Grow a decision tree split by split and see how impurity guides every choice.

• Clustering Visualizer

  Run K-means and DBSCAN step by step on blobs, moons and rings.

• Confusion Matrix Explorer

  Slide a decision threshold and watch precision, recall and the ROC curve trade off.

• Fairness Lab

  Set loan-approval thresholds for two groups and discover why every fairness metric can’t be satisfied at once.

• Image Filter Playground

  Apply convolution kernels pixel by pixel and see the edge detectors CNNs learn on their own.

• Object Detection Lab

  Run a real COCO-SSD detector in your browser on your camera or sample images.

• Text Playground

  Tokenize text, score sentiment and explore how words become vectors.

• Recurrent Networks Lab

  Step a recurrent network through a sequence and watch its memory fade or hold.

• Attention Visualizer

  Compute queries, keys and values by hand and see which words attend to which.

• Transformer Explorer

  Follow a sentence through embeddings, attention, feed-forward layers and out as a prediction.

• Diffusion Lab

  Add noise to an image step by step, then run the process in reverse to generate one.

• Pathfinding Visualizer

  Draw a maze and watch A*, Dijkstra, BFS and DFS explore it cell by cell.

• Travelling Salesman

  Place cities and pit greedy search, 2-opt, simulated annealing and genetic algorithms against each other.

• Reinforcement Learning Arena

  Train a Q-learning agent in a gridworld and watch its value map form episode by episode.

• Next-Token Sampling Lab

  Train a tiny language model in your browser, then steer its output with temperature, top-k and top-p.

• Embedding Explorer

  Embed a set of documents, project them to 2D, and run a semantic search you can inspect step by step.

• Calibration Lab

  Does 80% confident mean right 80% of the time? Build a reliability diagram and fix an overconfident model.

• Boosting Lab

  Add trees one at a time and watch each new one fix the mistakes of the ensemble so far.

• Recommender Lab

  Rate a few films, train matrix factorization live, and see your taste as a point in the same space as the films.

• Model Compression Lab

  Shrink a trained network with low-rank factorization and quantization and see exactly what accuracy it costs.

• Tokenizer Lab

  Train a byte-pair tokenizer on your own text, watch merges form, and see why models stumble on spelling and arithmetic.

• Reward Model Lab

  Judge pairs of answers, train a reward model on your preferences, then watch a policy optimise it and start to game it.

• Mixture of Experts Lab

  Train a small mixture-of-experts network live and watch the router carve the data between specialists.