Introduction

Over the years, I’ve explored a range of books covering artificial intelligence, machine learning, data science, and the broader societal implications of automation.
Below is a curated list of some standout reads from my Goodreads library, along with a few thoughts and takeaways. I asked ChatGpt to create a post from my GoodReads reading list

📚 Featured AI & ML Books

The Alignment Problem: Machine Learning and Human Values

Author: Brian Christian
Published: 2020
Average Rating: 4.34
My Rating: —
A deep dive into the ethical and philosophical challenges of aligning AI systems with human values. Brian Christian connects cutting-edge research with real-world implications.

The AI Con: How to Fight Big Tech’s Hype and Control AI’s Future

Author: Emily M. Bender
Published: 2025
Average Rating: 3.82
My Rating: —
An essential critique of AI hype, this book dismantles misconceptions about large language models and challenges how we think about AI governance.

Deep Learning with Python

Author: François Chollet
Published: 2017
Average Rating: 4.57
My Rating: —
Written by the creator of Keras, this book remains one of the most accessible yet powerful introductions to deep learning concepts and practice.

Machine Learning Interviews: Kickstart Your Machine Learning Career

Author: Susan Shu Chang
Published: 2024
Average Rating: 4.07
My Rating: —
A hands-on guide for aspiring ML engineers, filled with practical examples, interview tips, and the mindset needed to succeed in technical roles.

Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide

Author: Daniel Voigt Godoy
Published: 2022
Average Rating: 4.58
My Rating: —
An approachable, project-based introduction to PyTorch — perfect for those learning deep learning through coding and experimentation.

AI Snake Oil: What Artificial Intelligence Can’t Do (Yet)

Author: Arvind Narayanan
Published: 2024
Average Rating: 3.92
My Rating: ★★★★★

“This book showed me how to be more critical of AI claims and to separate genuine innovation from hype.”
A concise and much-needed guide for understanding the limits and risks of modern AI systems.

Not with a Bug, But with a Sticker: Attacks on Machine Learning Systems and What We Can Do About Them

Authors: Ram Shankar Siva Kumar
Published: 2023
Average Rating: 4.31
My Rating: —
A fascinating look at the security and robustness of ML systems — and how adversarial attacks challenge their reliability.

Life 3.0: Being Human in the Age of Artificial Intelligence

Author: Max Tegmark
Published: 2017
Average Rating: 4.00
My Rating: —
A philosophical and forward-looking exploration of AI’s future impact on society, consciousness, and existence itself.

Calling Bullshit: The Art of Skepticism in a Data-Driven World

Authors: Carl T. Bergstrom & Jevin D. West
Published: 2020
Average Rating: 4.11
My Rating: —
Not strictly about AI, but essential reading for anyone navigating data claims, algorithms, and misinformation.

Data Science from Scratch: First Principles with Python

Author: Joel Grus
Published: 2015
Average Rating: 3.91
My Rating: ★★★★★
A back-to-basics guide that builds intuition for data science and machine learning from the ground up — no libraries, just Python and logic.

🧠 Final Thoughts

Reading across these works has helped me appreciate both the power and limitations of artificial intelligence.
From ethical questions to hands-on coding, each book offers a different lens on how we can responsibly build and use intelligent systems.

If you’re looking to start your own AI/ML reading journey, these titles make a perfect foundation.