PEARSON EDUCATION Elements of ML Programming, ML97 Edition

Machine Learning: An Algorithmic Perspective, Second Edition

CRC Press

ML for the Working Programmer

Cambridge University Press

Machine Learning and Artificial Intelligence

Springer

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

Packt Publishing

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

MIT Press

Beginning Mathematica and Wolfram for Data Science: Applications in Data Analysis, Machine Learning, and Neural Networks

Apress

Machine Learning: A Constraint-Based Approach

Morgan Kaufmann

Machine Learning – A Probabilistic Perspective

MIT Press

Machine Learning for Text

Springer

Machine Learning

Springer

Machine Learning

Springer

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

MIT Press

Machine Learning for Computer Scientists and Data Analysts: From an Applied Perspective

Machine Learning for Science and Engineering: Volume I: Fundamentals (Course Notes)

Machine Learning for Text

Springer

Introduction to Programming Using SML (International Computer Science Series)

ADDISON-WESLEY

Mathematics and Programming for Machine Learning with R: From the Ground Up

CRC Press

Mathematics and Programming for Machine Learning with R: From the Ground Up

CRC Press

Applied Machine Learning for Data Science Practitioners

Artificial Intelligence: With an Introduction to Machine Learning, Second Edition (Chapman & Hall/CRC Artificial Intelligence and Robotics Series)

CRC Press

Machine Learning in Production: From Models to Products

MIT Press

Elements of Dimensionality Reduction and Manifold Learning

Springer

Introduction to Machine Learning: From Math to Code

Cambridge University Press

Machine Learning with Python: Theory and Implementation