Principles of Data Science

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

Machine Learning Algorithms: Handbook: A step-by-step guide to all Machine Learning algorithms with implementation using Python!

Feature Engineering Made Easy: Identify unique features from your dataset in order to build powerful machine learning systems

Dataclysm: Love, Sex, Race, and Identity--What Our Online Lives Tell Us about Our Offline Selves

Hands-On Machine Learning for Cybersecurity: Safeguard your system by making your machines intelligent using the Python ecosystem

AI Engineering: Building Applications with Foundation Models

On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines

Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more

LLM Engineer's Handbook: Master the art of engineering large language models from concept to production

Fundamentals of Data Engineering: Plan and Build Robust Data Systems

HBR Guide to AI Basics for Managers

Predictive Analytics Applications with WEKA

Deciphering Data Architectures: Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh

Practical Lakehouse Architecture: Designing and Implementing Modern Data Platforms at Scale

Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics

Driving Data Quality with Data Contracts: A comprehensive guide to building reliable, trusted, and effective data platforms

A Thousand Brains: A New Theory of Intelligence

What Is Data Science?

Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications