Recommended reading

• Hefferon, J. (2017). Linear Algebra.
  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.
  Available online

• 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.).
  A highly accessible introduction to statistical learning, including clear explanations and examples of regularization techniques such as ridge regression and the lasso. Available online for free.

• 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. A technical yet accessible explanation of the ensemble of models that won the Netflix Prize, detailing matrix factorization, neighborhood models, and hybrid ensemble techniques.
  Available online