Is there a book analogous to
Introduction to Algorithms (CLRS) for Machine Learning, that covers all the models and explanations comprehensively?
My recommendation is Applied Predictive Modeling by Max Kuhn. This book covers all the major regression and classification models, as well as some lesser known models, such as Cubist and C5.0. While the text does not cover deep learning, it does cover caret, which is Kuhn's R package. The caret package contains several deep learning models (e.g. stacked autoencoders) as well as many other classification and regression models.