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A course for each subject on the roadmap. Each course is split into the ideas its problems need, with articles and videos for each one. Every problem links to the parts to read first.

Linear AlgebraMatrices, projections and eigenvalues, the language the rest is written in.7 sections / 17 resources / 9 videos
CalculusGradients and the chain rule, the machinery inside every training loop.7 sections / 15 resources / 4 videos
Probability and StatisticsDistributions, covariance, and the distances between distributions.8 sections / 17 resources / 4 videos
Machine LearningFit models by hand: regression, regularisation, trees, margins, validation.8 sections / 21 resources / 13 videos
Evaluation MetricsScore a model honestly, especially when the classes are imbalanced.7 sections / 17 resources / 3 videos
Unsupervised LearningFind structure without labels: clustering, projection, and scoring both.6 sections / 14 resources / 4 videos
Deep LearningActivations, backprop, optimisers, normalisation, recurrence, attention.8 sections / 20 resources / 5 videos
Natural Language ProcessingTokenisation, term weighting, position, and translation quality.7 sections / 15 resources / 2 videos
Computer VisionConvolution, pooling, box overlap and duplicate suppression.7 sections / 15 resources / 1 videos
PyTorchAutograd, optimisers and training loops in a real framework.6 sections / 13 resources
Information TheoryEntropy at work: conditional entropy, mutual information, divergences, codes.6 sections / 15 resources / 1 videos
OptimisationMomentum, adaptive steps, schedules and line search: how training moves downhill.6 sections / 15 resources
Ensembles and BoostingCombine weak models: voting, bagging, AdaBoost and gradient boosting.6 sections / 14 resources / 5 videos
Probabilistic ModelsMarkov chains, hidden Markov models, mixtures and Bayesian regression.6 sections / 13 resources / 4 videos
Reinforcement LearningReturns, Bellman updates, Q-learning, advantages and bandits.7 sections / 17 resources / 1 videos
Recommender SystemsCollaborative filtering, matrix factorisation and ranking metrics.6 sections / 13 resources
Time SeriesSmoothing, autocorrelation, autoregression and sequence alignment.6 sections / 17 resources / 2 videos
MLOps and Data ProcessingClean, split and encode data, watch it drift, and ship models small.7 sections / 16 resources
Generative ModelsVAEs, GANs, normalising flows and diffusion, one equation at a time.6 sections / 15 resources / 2 videos
Graph Neural NetworksMessage passing on graphs: GCN, GraphSAGE, graph attention and PageRank.7 sections / 16 resources / 1 videos
Transformers and LLMsThe parts of a modern language model: masks, RoPE, RMSNorm, caches, sampling.7 sections / 15 resources / 2 videos