First edition · August 2026
The AI Search Handbook
A complete, self-contained guide to generative engine optimisation: how AI search retrieves, chunks, evaluates and cites information, and how to become a source an AI answer names.
2026 edition · by Luke McLaughlin
AI Engineering Essentials
A guided AI engineering course with executed practice projects
Sixteen chapters from NumPy and data preparation through classical machine learning, neural networks and PyTorch to NLP, embeddings, transformers, RAG and MLOps — every project with runnable code and its exact output.
- Pages
- 854
- Chapters
- 16
- Format
- Projects
- Executed
Free download. No payment, no sign-up. Analysts, marketers and developers who want to understand and build with machine learning and LLMs.
About this book
What it covers
AI Engineering Essentials turns AI engineering into a guided course: full concept lessons, worked traces, visual models, checkpoints, quizzes and executed practice projects.
Every project keeps the same rhythm — read the data shape, name the transformation, predict the metric or output, then change one detail safely — so each habit transfers to your own work.
What you will be able to do
- NumPy, pandas and data preparation that holds up
- Evaluation metrics and classical ML with scikit-learn
- Gradient descent and neural networks from scratch
- PyTorch, vision and NLP fundamentals
- Embeddings, similarity search and transformers
- LLM engineering, prompts, RAG, scaling and MLOps
Contents
Sixteen chapters
- Ch. 1NumPy Fundamentals
- Ch. 2NumPy Math, Broadcasting & Linear Algebra
- Ch. 3pandas Essentials
- Ch. 4Data Preparation & Feature Engineering
- Ch. 5Evaluation Metrics from Scratch
- Ch. 6Classical ML with scikit-learn
- Ch. 7Regression & Classification from Scratch
- Ch. 8Optimization & Gradient Descent
- Ch. 9Neural Networks from Scratch
- Ch. 10PyTorch Fundamentals
- Ch. 11Vision: Images as Arrays
- Ch. 12NLP & Text Processing
- Ch. 13Embeddings & Similarity Search
- Ch. 14Transformers & Attention from Scratch
- Ch. 15LLM Engineering, Prompts & RAG
- Ch. 16Scaling, Compute, Cost & MLOps
Written by
Luke McLaughlin
Luke is an AI marketing and search specialist with more than ten years in B2B marketing across biotech, life sciences and SaaS. His work sits where technical SEO meets generative engine optimisation: making brands crawlable, retrievable and cited by AI answer engines.
He holds an MSc in Molecular Biology, works hands-on in Python, SQL and both cloud and local open-weight language models, and writes these books so that marketers can do the same.
Also free
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