Recommended reading
Hefferon, J. (2017). Linear Algebra.1
A free and accessible open-source textbook designed for beginners, with clear explanations, worked examples, and plenty of exercises. Ideal for self-learners or students seeking to build both intuition and practical skills.Strang, G. (2019). Linear Algebra and Learning from Data.
Focuses on the role of linear algebra in modern machine learning and data science. A practical companion to statistical and algorithmic methods.Seber, G. A. F. (2003). Linear Regression Analysis (2nd ed.).
A concise and mathematically rigorous exploration of regression, grounded in matrix algebra. Offers expanded coverage of diagnostics, model fitting, selection, and prediction.James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning (2nd ed.).2
A highly accessible introduction to statistical learning, including clear explanations and examples of regularization techniques such as ridge regression and the lasso.Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.).
The authoritative reference on regularization and other machine learning techniques. More theoretical and in-depth, suited for readers looking for a deeper mathematical understanding.Koren, Y., Bell, R., & Volinsky, C. (2009). The BellKor Solution to the Netflix Prize.3
A technical yet accessible explanation of the ensemble of models that won the Netflix Prize, detailing matrix factorization, neighborhood models, and hybrid ensemble techniques.