Papers & Books
In-depth technical guides, research papers, and educational books on Machine Learning, AI engineering, and data science — written to bridge theory with real-world application.
Machine Learning for Everyone
From Zero to Building Your First Working Model
A complete beginner's guide to Machine Learning — no maths degree required. Covers the 75-year history of ML, all three learning paradigms, every major algorithm explained with real-life analogies, and a step-by-step workflow to build and deploy your first model. Includes a full working Python example and a curated 8-week learning roadmap.
Machine Learning with Keras
The Friendly Architect of Neural Networks
A deep-dive technical guide covering the complete Keras ecosystem for building production-grade deep learning models. Covers all three model-building APIs (Sequential, Functional, Subclassing), CNNs, RNNs, Transfer Learning, custom callbacks, advanced architectures (GANs, Transformers, Autoencoders), and model deployment. Includes 10 original data visualisations.
Data Science for Hardware Business
Profit Protection, Forecasting & Production ML on AWS
A professional handbook for the Hybrid Data Scientist & Strategic Business Analyst role in hardware-focused organisations. Covers price elasticity modelling, gray market arbitrage detection, demand forecasting with LSTMs and XGBoost, Multi-Armed Bandit pricing experiments, OEM negotiation analytics, IIoT anomaly detection, and end-to-end AWS SageMaker MLOps pipelines.
AWS Certified AI Practitioner (AIF-C01)
The One-Shot Complete Study Guide
A single, exhaustive reference for the AWS Certified AI Practitioner exam. Covers all five official domains: AI and ML fundamentals, generative AI, applications of foundation models, responsible AI, and security and governance. Includes comparison tables, exam tips and key-concept callouts.
Snowpark for Snowflake
Complete Interview Study Guide
A complete interview-preparation guide to Snowpark in Python, Java and Scala. Covers sessions, the DataFrame API, UDFs and stored procedures, data loading, Snowpark ML, performance optimisation and advanced topics, with an interview Q&A bank, code examples and a quick-reference cheat sheet.