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Video lessons

One 3D animated story for every week. Watch it first, then read the lesson. Footage (Veo) and voice are made with Google Gemini from the lesson text.

MLF · Week 1What is machine learning, and which maths do we need?▶MLF · Week 2Slopes, change and the steepest way up▶MLF · Week 3Vectors, matrices and the best-fit line▶MLF · Week 4The special directions of a matrix▶MLF · Week 5Symmetric matrices: the well-behaved ones▶MLF · Week 6SVD and PCA: squeezing data▶MLF · Week 7Finding the lowest point: gradient descent▶MLF · Week 8Bowls, not bumpy hills: convexity▶MLF · Week 9Optimising with rules: Lagrange and KKT▶MLF · Week 10Probability for machine learning▶MLF · Week 11The exponential family▶MLF · Week 12Estimating parameters, and EM▶MLT · Week 1PCA: finding the best view of your data▶MLT · Week 2Kernel PCA: curved views of data▶MLT · Week 3K-means: grouping without labels▶MLT · Week 4MLE, Bayes, Gaussian mixtures and EM▶MLT · Week 5Regression: least squares and its Bayesian view▶MLT · Week 6Ridge and LASSO: taming overfitting▶MLT · Week 7k-NN and decision trees▶MLT · Week 8Generative models: Naive Bayes▶MLT · Week 9Perceptron and logistic regression▶MLT · Week 10Support vector machines▶MLT · Week 11Bagging, boosting and AdaBoost▶MLT · Week 12Neural networks▶MLP · Week 1Your first end-to-end ML project▶MLP · Week 2The project continued: pipelines and tuning▶MLP · Week 3Linear regression and gradient descent in sklearn▶MLP · Week 4Polynomial regression and regularisation▶MLP · Week 5Logistic regression in sklearn▶MLP · Week 6Binary classifiers and how to judge them▶MLP · Week 7Multiclass classifiers▶MLP · Week 8SVMs in sklearn▶MLP · Week 9Decision trees and ensembles▶MLP · Week 10Random forests, boosting and tuning▶MLP · Week 11Neural networks in sklearn▶MLP · Week 12Unsupervised learning in sklearn▶