Python Machine Learning By Example: Implement machine learning algorithms and techniques to build intelligent systems, 2nd Edition, Edition 2

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

Deep Learning with R for Beginners: Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet

AI Engineering: Building Applications with Foundation Models

PyTorch 1.x Reinforcement Learning Cookbook: Over 60 recipes to design, develop, and deploy self-learning AI models using Python

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

Fundamentals of Data Engineering: Plan and Build Robust Data Systems

Hands-On Deep Learning Architectures with Python: Create deep neural networks to solve computational problems using TensorFlow and Keras

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

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

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

Machine Learning for Hackers: Case Studies and Algorithms to Get You Started

What Is Data Science?

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

AI for Absolute Beginners: A Human-Friendly Guide to Understanding Artificial Intelligence

Before Machine Learning Volume 1 - Linear Algebra for A.I: The Fundamental Mathematics for Data Science and Artificial Intelligence

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

Managing Data as a Product: Design and build data-product-centered socio-technical architectures

Python: Master the Art of Design Patterns

Practical Guide to Applied Conformal Prediction in Python: Learn and apply the best uncertainty frameworks to your industry applications