Reading List¶

Optional further reading to take your ML skills to the next level.

Books¶

Title	Author(s)	Focus
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow	Aurélien Géron	Practical end-to-end ML with code
An Introduction to Statistical Learning (ISLR)	Gareth James et al.	Statistical foundations of ML (free PDF available)
Interpretable Machine Learning	Christoph Molnar	Model explainability techniques (free online)
The Elements of Statistical Learning	Hastie, Tibshirani, Friedman	Advanced statistical ML theory (free PDF available)
Python Data Science Handbook	Jake VanderPlas	NumPy, Pandas, Matplotlib, Scikit-Learn (free online)

Resource	Link	Focus
Scikit-Learn User Guide	scikit-learn.org	Official documentation and examples
StatQuest (YouTube)	Josh Starmer	Visual explanations of ML concepts
Kaggle Learn	kaggle.com/learn	Free hands-on ML tutorials
Machine Learning Mastery	machinelearningmastery.com	Practical tutorials with Python
fast.ai	fast.ai	Practical deep learning course (free)

XGBoost: Chen & Guestrin, 2016 — "XGBoost: A Scalable Tree Boosting System"
SHAP: Lundberg & Lee, 2017 — "A Unified Approach to Interpreting Model Predictions"
Random Forests: Breiman, 2001 — "Random Forests"

KSB	Description	How This Addresses It
K1	Context of Data Science	Understanding where ML sits within the broader discipline
S3	Programming languages and tools	Setting up the development environment and dependencies
B6	Commitment to keeping up to date	Engaging with current ML resources and research