// Course · stage 2 of 9
Classical Machine Learning
Supervised and unsupervised learning, evaluation and generalisation, plus trees, SVMs, clustering and dimensionality reduction.
4 unitsabout 32 hours20 algorithms
01Units in this stage
In order. Each unit has goals, algorithms, must-read papers, a project and a self-test.
01 · U20 The ML Workflow: Data, Loss & Generalization Train/validation/test splits, overfitting, bias–variance, cross-validation and metrics. 10 h3 algorithms1 paperslesson ready 02 · U21 Trees & Ensembles Decision trees, random forests, gradient boosting, XGBoost/LightGBM — the kings of tabular data. 8 h6 algorithms3 papers 03 · U22 Linear Models, SVM & Nearest Neighbors From the perceptron to maximum margin and the kernel trick, plus k-nearest neighbours. 6 h5 algorithms2 papers 04 · U23 Unsupervised Learning k-means, Gaussian mixtures and EM, PCA, t-SNE/UMAP. 8 h6 algorithms2 papers
02Algorithms you will meet
Algorithms Atlas →Linear RegressionLogistic RegressionStochastic Gradient Descent (SGD)Decision Trees (CART)Random ForestGradient Boosting (GBDT)XGBoostLightGBMAdaBoostPerceptronSupport Vector Machinek-Nearest NeighborsRegularized Regression (Lasso / Ridge)Naive Bayesk-Means ClusteringExpectation–Maximization (EM)Principal Component Analysis (PCA)t-SNEUMAPDBSCAN (Density-Based Clustering)
Part of these routes: Algorithm Researchers · Domain Scientists