CRC Press Machine Learning: An Algorithmic Perspective, Second Edition

Machine Learning: Theory and Practice

CRC Press

Foundations of Machine Learning (Adaptive Computation and Machine Learning series)

MIT Press

Machine Learning: A Constraint-Based Approach

Morgan Kaufmann

Mathematics of Machine Learning: Master linear algebra, calculus, and probability for machine learning

Packt Publishing

Probability and Statistics for Computer Science

Springer

Machine Learning – A Probabilistic Perspective

MIT Press

Machine Learning

Springer

Machine Learning

Springer

A First Course in Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Chapman and Hall/CRC

Fundamentals of Machine Learning

Oxford University Press

Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

Packt Publishing

The Hundred-Page Machine Learning Book

Andriy Burkov

Model-Based Machine Learning

Chapman and Hall/CRC

Applied Machine Learning

Springer

Machine Learning Algorithms in Depth

Deep Learning for Physical Scientists: Accelerating Research with Machine Learning

Wiley

Fundamental Mathematical Concepts for Machine Learning in Science

Springer

Understanding Machine Learning: From Theory to Algorithms

Cambridge University Press

Machine Learning: A Practical Approach on the Statistical Learning Theory

Springer

A Greater Foundation for Machine Learning Engineering: The Hallmarks of the Great Beyond in Pytorch, R, Tensorflow, and Python

Authorhouse

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems

O'Reilly

Applied Machine Learning Using mlr3 in R

Chapman and Hall/CRC

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

Packt Publishing

Machine Learning Foundations: Volume 1: Supervised Learning

Addison Wesley