📚 The Simple Machine Learner's Library

These are the essential books machine learning engineers should read to learn the topics they need to know about building machine learning systems. This list is comprehensive but purposefully kept short to list only what you need.

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🧠 Machine Learning Fundamentals

Mathematical foundations for ML

• Mathematics of Machine Learning by Tivadar Danka

  • Essential mathematical concepts for ML
  • Linear algebra, calculus, and probability theory
  • Published by Cambridge University Press with free online version

• Deep Learning Workbook by Tom Yeh, Mohsena Ashraf

  • 300 ML math puzzles for hands-on practice
  • Learn AI math by calculating it by hand

Understanding core ML concepts and algorithms

• The Hundred-Page Machine Learning Book by Andriy Burkov

  • Concise overview of ML concepts
  • Quick reference for practitioners
  • Perfect starting point for ML newcomers

• Machine Learning Q and AI by Sebastian Raschka

  • 30 essential questions and answers on ML and AI
  • Advanced concepts beyond introductory material
  • Question-and-answer format for deep understanding

System Design

• Designing Data-Intensive Applications by Martin Kleppmann

  • Fundamental concepts for data systems (focus on Chapters 5 & 6)
  • Essential for ML data infrastructure
  • Written by Cambridge University researcher and former LinkedIn engineer

• Designing Machine Learning Systems by Chip Huyen

  • End-to-end ML system design
  • Production considerations and best practices
  • Written by former NVIDIA/Netflix/Google ML engineer

• Reliable Machine Learning by Cathy Chen, Niall Richard Murphy, Kranti Parisa, D. Sculley, Todd Underwood

  • Applying SRE principles to ML systems
  • Reliability and observability in ML
  • Google's approach to production ML

⚒️ Hands-on

• Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili

  • Comprehensive guide to ML with modern Python libraries
  • Covers both traditional ML and deep learning approaches
  • 4th edition evolution of "Python Machine Learning"

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron

  • Practical approach to ML with code examples
  • Perfect bridge between theory and implementation
  • Industry-standard reference for ML practitioners

• Deep Learning with Python by François Chollet

  • Practical deep learning with Keras
  • Written by the creator of Keras and Google AI researcher
  • Perfect introduction to deep learning concepts

• Build a Large Language Model (from Scratch) by Sebastian Raschka

  • Step-by-step guide to creating your own LLM
  • From design and pretraining to fine-tuning for specific tasks
  • Uses Python and PyTorch with practical examples

💼 Interview Preparation

Technical interview success

• Elements of Programming Interviews in Python by Adnan Aziz, Tsung-Hsien Lee, and Amit Prakash

  • Essential coding interview preparation (make sure to use the language you're interviewing in)
  • Over 250 programming questions and solutions
  • Written by former Google/Microsoft/Facebook engineers

• System Design Interview by Alex Xu

  • Systematic approach to system design interviews
  • Scalable system architecture principles
  • Former Twitter/Apple/Zynga engineer's insights

• Inside the Machine Learning Interview by Peng Shao

  • 151 real questions from FAANG companies with detailed answers
  • Covers ML fundamentals, coding, system design, and infrastructure
  • Written by former Amazon Alexa founding team member and Twitter Staff ML Engineer

• ML System Design Interview by Ali Aminian, Alex Xu

  • Systematic approach to ML system design interviews
  • Real-world case studies and solutions
  • 7-step framework for ML system design questions

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