Best AI & Machine Learning Books for Beginners to Intermediates

In this post, we’ve curated book recommendations based on experience and community feedback. The books listed here are trusted entry points into AI and machine learning, recommended by practitioners, educators, and researchers.
To help you find your ideal starting point, books are organized into categories. We recommend complete beginners to pick their first book from the Practical Applied Machine Learning section.

We’ll keep enriching and updating this list as the field evolves.

Disclosure: This page contains affiliate links.

📖 Book Categories

The AI Encyclopedia (Comprehensive Overview)
Practical Applied Machine Learning
Deep Learning
Reinforcement Learning
Mathematical Foundations for AI

AI Foundations & The Big Picture

Artificial Intelligence: A Modern Approach (4th Edition, 2020) – Stuart Russell & Peter Norvig (AIMA)

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Often called “the AI bible”, this is the definitive textbook on artificial intelligence. It covers everything from search algorithms, logic, and planning to modern machine learning and robotics, providing the broadest possible view of AI as a discipline.

✅ Good for:

Comprehensive coverage of the entire AI field
Clear explanations of search, reasoning, planning, and learning
Understanding the historical and conceptual context of AI

⚠️ Weak at:

Very broad. Can feel overwhelming for complete beginners
Limited hands-on practical material and modern deep learning depth

👤 Best for: Students and readers seeking systematic understanding of AI fundamentals and how all the pieces connect. Essential companion for intro to AI courses.

Practical Applied Machine Learning (Good for Complete Beginners)

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2022, 3rd ed.) – Aurélien Géron

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A project-driven introduction covering both classical ML and deep learning with TensorFlow.

✅ Good for:

Practical, end-to-end ML workflows (preprocessing → training → deployment)
Clear coverage of both classical ML (Scikit-Learn) and deep learning (TensorFlow/Keras)
Beginners who learn best by coding and building projects

⚠️ Weak at:

TensorFlow-centric (PyTorch readers may prefer the alternative below)
Lighter on math theory and proofs

👤 Best for: Programmers entering ML who want to learn by building projects

Hands-On Machine Learning with Scikit-Learn and PyTorch: Concepts, Tools, and Techniques to Build Intelligent Systems (2025) – Aurélien Géron

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PyTorch edition of Géron’s popular ML guide (the book above this one). Maintains the same clear, example-driven style while updating deep learning chapters for PyTorch and covering recent ML advances.

✅ Good for:

Learning practical ML workflows with an emphasis on real-world projects
Readers who prefer PyTorch over TensorFlow
Clear explanations that balance intuition and implementation
Engineering-focused, code-first approach to building systems

⚠️ Weak at:

Applied practice over deep theoretical foundations
PyTorch-focused (TensorFlow readers should choose the edition above)

👤 Best for: Developers and practitioners who want a hands-on, end-to-end guide to machine learning and deep learning using modern tools

An Introduction to Statistical Learning: With Applications in R (2nd Edition, 2021) – Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (ISL)

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A friendly, concept-driven introduction to classical machine learning methods such as regression, classification, and resampling. The second edition expands coverage with modern topics like deep learning and tree-based methods.
Often recommended as a gentle introduction to ML before tackling deeper texts like its sibling book Elements of Statistical Learning (ESL).

✅ Good for:

Building strong intuition for classical ML algorithms
Readers who prefer conceptual understanding over heavy math
Hands-on examples (in R) that reinforce key ideas
Statistics-focused approach to understanding model interpretation and trade-offs

⚠️ Weak at:

Deep learning coverage is introductory (emphasis is on classical ML)
Examples are in R (some readers may prefer Python-based guides)
You’ll need another source if you’re looking to dive deep into the math

👤 Best for: Beginners and practitioners who want to understand the logic behind ML models before diving into more advanced books

Deep Learning

Deep Learning (2016) – Ian Goodfellow, Yoshua Bengio, Aaron Courville

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Known as “The DL Bible“. The definitive theoretical textbook on deep learning, written by three leading researchers in the field. Provides comprehensive mathematical foundations covering neural networks, optimization, CNNs, RNNs, and regularization techniques.

✅ Good for:

Rigorous mathematical foundations and theory
Understanding the “why” behind deep learning algorithms
Comprehensive coverage of pre-2016 architectures (CNNs, RNNs, LSTMs)
Building deep conceptual understanding of core principles

⚠️ Weak at:

Dense and math-heavy (requires strong calculus, linear algebra, probability background)
Published before transformers (no coverage of attention mechanisms, BERT, GPT, or modern LLMs)
Lacks practical implementation guidance and modern frameworks

👤 Best for: Graduate students, researchers, and serious practitioners who want rigorous theoretical understanding of deep learning fundamentals

Dive into Deep Learning (1st Edition, 2023) – Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola

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A fully interactive, open-source deep learning book that blends theory with code. Dive into Deep Learning (D2L) teaches core deep learning concepts with implementations in PyTorch, TensorFlow, JAX, and MXNet, complete with runnable Jupyter notebooks.

Updated continuously online for free at d2l.ai

✅ Good for:

Learning deep learning through hands-on code examples
Readers who prefer an applied, experiment-driven approach
Modern architectures and frameworks (transformers, attention, etc.)
Staying current with updates to the online version

⚠️ Weak at:

Less mathematical depth than Goodfellow et al.
Code-focused. Not ideal for purely theoretical learners
Online-first format. Printed version may lag behind updates

👤 Best for: Learners who want to understand deep learning by implementing concepts, not just reading theory

Reinforcement Learning

Reinforcement Learning: An Introduction (2018, 2nd ed.) – Richard Sutton & Andrew Barto

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Known as “The RL Bible“. The foundational RL textbook, universally used in top university courses. Covers core RL concepts from multi-armed bandits and MDPs to temporal-difference learning, Monte Carlo methods, and policy gradients. The second edition adds function approximation and introduces deep RL basics.

✅ Good for:

Clear, intuitive explanations of core RL algorithms (Q-learning, SARSA, TD, etc.)
Building theoretical foundations in RL from first principles
Following along with university courses or video lectures
Understanding the “why” behind RL methods

⚠️ Weak at:

Minimal code implementations (mostly pseudocode)
Limited coverage of deep RL
Math-heavy. Beginners may prefer starting with Grokking Deep RL below

👤 Best for: Learners with calculus and probability background who want to understand RL theory deeply

Grokking Deep Reinforcement Learning (2020) – Miguel Morales

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A practical, intuitive introduction to RL that bridges theory and implementation. Offers a gentler entry point than Sutton & Barto’s foundational text, with more code and less math. Walks through core concepts using clear explanations and PyTorch examples.

✅ Good for:

Building intuition through well-structured, incremental examples
Learning RL concepts hands-on with PyTorch
Readers who prefer code-driven understanding over abstract math

⚠️ Weak at:

Limited coverage of recent large-scale or transformer-based RL methods
Simplified math. Less suitable for readers seeking deep theoretical depth

👤 Best for: Beginners or intermediate practitioners who want a clear, friendly introduction to reinforcement learning through working code examples

Mathematical Foundations for AI

Mathematics for Machine Learning (2020) – Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong

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A comprehensive introduction to the mathematical foundations of ML, covering linear algebra, vector calculus, probability, and optimization, motivated by concrete ML applications. Designed to teach the math you actually need for understanding ML algorithms, rather than generic math courses.

✅ Good for:

Building rigorous mathematical foundations for ML
Understanding the “why” behind ML algorithms mathematically
Gaining a strong background before tackling other ML and RL books

⚠️ Weak at:

Can be challenging without prior calculus and linear algebra exposure
Requires significant time investment and active problem-solving

👤 Best for: Students and practitioners who want to strengthen their mathematical understanding before diving deep into ML theory

Final Thoughts

These books are among the most trusted and widely recommended starting points for building a solid foundation in AI and machine learning.
We’ll continue to expand this list as the field evolves. If you notice something missing or outdated, feel free to reach out at aipapers@aipapersacademy.com.

Stay tuned for our upcoming list of advanced AI books.