Machine Learning
Foundations and practical algorithms, from linear regression and logistic regression to decision trees, ensembles, clustering, and K-nearest neighbours.
Artificial Intelligence
Explore the AI topics in this handbook. Click a subcategory to find beginner-friendly guides, worked examples, and quick references.
Artificial Intelligence Subcategories
Advanced Machine Learning
Support Vector Machines, Naive Bayes, time series, and other advanced topics explained step by step.
Deep Learning
Neural networks, CNNs, RNNs, LSTMs, and PyTorch end-to-end examples, all taught with a consistent spam-classifier example.
Computer Vision
Object detection, localisation, anchor boxes, region-based and one-stage detectors, YOLO, SSD, and practical YOLO11 demonstrations.
Generative AI
Large language models, LangChain, RAG, agents, and building AI-powered applications in plain language.
Natural Language Processing
Teaching computers to understand human language: text processing, classical text generation, and more.
Time Series
Stationarity, decomposition, simple forecasting models, exponential smoothing, and autoregressive models.
Model Evaluation
Train-test splits, cross-validation, metrics, overfitting, hyperparameter tuning, and honest model validation.
Data Science
Statistics, exploratory data analysis, hypothesis testing, and data-analysis fundamentals.
GenAI Dev Tools
IDEs and tools for building with generative AI, such as Windsurf and Cursor.