Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - Provides a rigorous theoretical foundation for machine learning, with Chapter 5 offering detailed explanations of supervised learning, model parameters, and representing models as functions.
Mathematics for Machine Learning, Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, 2020 (Cambridge University Press)DOI: 10.1017/9781108679989 - Specifically designed to cover the mathematical prerequisites for machine learning, including linear algebra, calculus, and probability, directly linking them to model formulation and optimization.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, 2022 (O'Reilly Media) - Offers a highly practical and accessible introduction to machine learning concepts, illustrating how models function, their parameters, and the role of data in training, with concrete examples.