Machine Learning Engineer Reading List

A curated collection of books to help you grow as a Machine Learning Engineer and Scientist.

Originally created to share books that have helped me in some way on my journey as a machine learning engineer (MLE), hopefully some of which will be useful to you too!

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The Books

Machine Learning

• Bishop: Deep Learning, Foundations and Concepts (free e-reader version).
• Hastie et al: The Elements of Statistical Learning.
• Deisenroth et al: Mathematics for Machine Learning.
• Goodfellow et al: Deep Learning.
• Sutton et al: Reinforcement Learning.
• Foster: Generative Deep Learning

Natural Language Processing

• Jurafsky et al: Speech and Language Processing.
• Tunstall et al: Natural Language Processing with Transformers.

Network Analysis

• Jurafsky et al: Speech and Language Processing.
• Tunstall et al: Natural Language Processing with Transformers.

Statistics

• Ellis: The Essential Guide to Effect Sizes.
• Lee: Baysian Statistics.
• Martin: Regression Models for Categorical and Count Data.

Data Mining

• Leskovec: Mining Massive Datasets.

Programming Fundamentals

• Knuth: The Art of Computer Programming V1, Fundamental Algorithms.
• Dijkstra: A Discipline of Programming
• Martin: Clean Code, A Handbook of Agile Software Craftsmanship.

Python

• Shaw: CPython Internals, Your Guide to The Python 3 Interpreter.
• Gorelick et al: High Performance Python.
• Okken: Python Testing with Pytest.

Management

• Larson: An Elegant Puzzle, Systems of Engineering Management.
• Reilly: The Staff Engineer's Path, A Guide for Individual Contributors Navigating Growth & Change.