The fourteen guides

One guide per university module, all to the same standard. Each is written for the exam rather than the reading list: every chapter builds the idea, works an example through line by line, then hands the question over to you. In six of the guides the marks are attributed move by move as well.

Mathematics for AI

Linear Algebra, Calculus, Probability and Optimisation

Programming for AI

Python, NumPy and Vectorisation for AI

Machine Learning

Regression, Classification, Ensembles and Evaluation

Neural Networks

Backpropagation, CNNs, RNNs and Training

Deep Learning and Generative AI

Transformers, LLMs, Alignment and Prompting

Natural Language Processing

Embeddings, Transformers and Language Tasks

Computer Vision

CNNs, Detection, Segmentation and Vision Transformers

Reinforcement Learning

Markov Decision Processes, Q-Learning, Policy Gradients and Deep RL

Data Science and Data Mining

Pipelines, Clustering, Association Rules and Model Evaluation

MLOps

Pipelines, Deployment, Monitoring and Governance

AI Ethics and Responsible AI

Fairness, Privacy, Accountability and Safety

AI in Business

Strategy, Analytics, Automation and Decision-Making

AI in Finance

Machine Learning for Trading, Risk, Fraud and Forecasting

AI in Healthcare

Clinical Machine Learning, Medical Imaging, Safety and Governance

Every guide has a free sample chapter. The full guides are free to read with Kindle Unlimited, or available as a Kindle ebook or paperback.