Introductory Books on Artificial Intelligence

1. Building Machine Learning Powered Applications

Building Machine Learning Powered Applications emphasizes practical implementation, focusing on how to apply machine learning models to real-world projects and address concrete engineering challenges.

2. Designing Machine Learning Systems

This book centers on system design, teaching how to integrate AI models into complex systems. It includes numerous real-world examples and practical advice.

3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

A classic introduction to machine learning, this book offers extensive hands-on projects. Upon completion, you will have mastered the entire process from data preprocessing to model deployment.

4. Deep Learning

Considered the bible of the machine learning field, this book systematically covers the theoretical foundations of neural networks, convolutional networks, recurrent networks, and generative models.

5. Pattern Recognition and Machine Learning

This book delves into the mathematical principles, algorithm design, and probabilistic modeling that underpin machine learning.

6. Probabilistic Machine Learning: An Introduction

Focusing on Bayesian methods and probabilistic graphical models, this book provides a foundational introduction to probabilistic machine learning.

7. Feature Engineering for Machine Learning

This book primarily discusses how to extract effective features from raw data to enhance model performance.

8. AI Engineering

This book explores how to build scalable, reliable, and robust AI systems. It covers not only machine learning training and deployment but also common engineering challenges such as version control, data management, and inference optimization.