Hands-on model building with Python and scikit-learn.
How neural networks turn inputs into predictions, how they learn with gradient descent, and how to train one in PyTorch.
Build, compare and evaluate a real classifier end to end, predicting penguin species from their measurements.
Andrew Ng's foundational machine learning programme: supervised and unsupervised learning, neural networks and practical advice, in Python.
Five courses on neural networks: training and tuning, structuring projects, convolutional networks, sequence models and transformers.
A free, code-first course by Jeremy Howard that has you training state-of-the-art models from the first lesson.
Google's free, fast-paced introduction to machine learning, with videos, visualisations and exercises.
55 in this topic
The simplest predictive model: how linear regression fits a line through data, how to read its coefficients, and when it breaks down.
Machine learning 2 min read 18 Sep 2026 For people
Despite its name, logistic regression is a classification method. How it produces probabilities and why it remains a strong baseline.
Machine learning 2 min read 17 Sep 2026 For people
How decision trees split data with simple questions, why they are easy to explain, and why single trees overfit.
Machine learning 2 min read 16 Sep 2026 For people
Why averaging many randomised decision trees produces a robust, accurate model with little tuning.
Machine learning 2 min read 15 Sep 2026 For people
How gradient boosting builds trees that correct each other's mistakes, and why it dominates tabular machine learning.
Machine learning 2 min read 14 Sep 2026 For people
A model that predicts by looking at the most similar examples it has seen. Simple, intuitive, and sensitive to scaling.
Machine learning 2 min read 13 Sep 2026 For people
How SVMs find the widest margin between classes, and how kernels let them draw curved boundaries.
Machine learning 2 min read 12 Sep 2026 For people
A fast probabilistic classifier built on a bold simplifying assumption — and why it still works well for text.
Machine learning 2 min read 11 Sep 2026 For people
How k-means groups unlabelled data into clusters, how to choose the number of clusters, and its main limitations.
Machine learning 2 min read 10 Sep 2026 For people
PCA compresses many correlated features into a few components that capture most of the variation. How it works and when to use it.
Machine learning 2 min read 9 Sep 2026 For people
Accuracy, precision, recall, F1, ROC AUC, MAE, RMSE: what each measures and how to choose one that matches the real cost of mistakes.
Machine learning 2 min read 8 Sep 2026 For people
How to read a confusion matrix and trade precision against recall by moving the decision threshold.
Machine learning 2 min read 7 Sep 2026 For people
How k-fold cross-validation gives a more reliable estimate of model performance, and the variants for grouped and time-series data.
Machine learning 2 min read 6 Sep 2026 For people
When one class is rare — fraud, defects, disease — standard training and metrics mislead. Techniques that help.
Machine learning 2 min read 5 Sep 2026 For people
Why many algorithms need features on comparable scales, the difference between standardisation and min-max scaling, and when to skip it.
Machine learning 2 min read 4 Sep 2026 For people
One-hot, ordinal, target and hashing encodings: how to turn categories into numbers without misleading your model.
Machine learning 2 min read 3 Sep 2026 For people
How penalising large coefficients reduces overfitting, and the practical difference between ridge, lasso and elastic net.
Machine learning 2 min read 2 Sep 2026 For people
The optimisation method behind most machine learning: how gradient descent works, and how to choose a learning rate.
Machine learning 2 min read 1 Sep 2026 For people
Filter, wrapper and embedded methods for choosing which features to keep, and why fewer good features often beat many.
Machine learning 2 min read 31 Aug 2026 For people
Why understanding model predictions matters, and practical techniques: coefficients, feature importance, SHAP and partial dependence.
Machine learning 2 min read 30 Aug 2026 For people