Books of Machine learning

  • Machine Learning Pocket Reference
    By Matthew Harrison

    Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data.

  • Deep Learning: Methods and Applications
    Deep Learning: Methods and Applications
    By Li Deng, Dong Yu

    Provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks

  • Learning from Data: A Short Course
    By Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin

    Learning from Data: A Short Course

  • The Hundred-page Machine Learning Book
    The Hundred-page Machine Learning Book
    By Andriy Burkov

    Provides a practical guide to get started and execute on machine learning within a few days without necessarily knowing much about machine learning.The first five chapters are enough to get you started and the next few chapters provide you ...

  • Mathematics for Machine Learning
    By Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong

    Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

  • Introduction to Machine Learning
    Introduction to Machine Learning
    By Etienne Bernard

    The math content is kept to a minimum to focus on what matters-applying the concepts in useful contexts. This book is sure to benefit anyone curious about the fascinating field of machine learning.

  • Deep Learning with Python
    Deep Learning with Python
    By Francois Chollet

    By the time you reach the end of this book, you will have become a Keras expert and will be able to apply deep learning in your own projects.

  • Optimization for Machine Learning
    Optimization for Machine Learning
    By Stephen J. Wright, Sebastian Nowozin, Suvrit Sra

    This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods.

  • The Voice in the Machine: Building Computers that Understand Speech
    The Voice in the Machine: Building Computers that Understand Speech
    By Roberto Pieraccini

    In The Voice in the Machine, Roberto Pieraccini examines six decades of work in science and technology to develop computers that can interact with humans using speech and the industry that has arisen around the quest for these technologies.

  • Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction
    Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction
    By Valentina Emilia Balas, Harsh S. Dhiman

    The specific topics of ramp event prediction and wake interactions are addressed in this book, along with forecasted performance.

  • Trends in Neural Computation

    Trends in Neural Computation

  • Machine Learning for Societal Improvement, Modernization, and Progress
    Machine Learning for Societal Improvement, Modernization, and Progress
    By Vishnu S. Pendyala

    "'Machine learning for societal improvement, modernization, and progress' portrays the application of Machine Learning that is playing a prominent role in improving the quality of life and the progress of civilization.

  • Pattern Recognition and Machine Learning
    By Christopher M. Bishop

    The field of pattern recognition has undergone substantial development over the years. This book reflects these developments while providing a grounding in the basic concepts of pattern recognition and machine learning.

  • Machine Learning: The Power and Promise of Computers That Learn by Example

    Machine Learning: The Power and Promise of Computers That Learn by Example

  • Machine Learning with Spark
    Machine Learning with Spark
    By Nick Pentreath

    If you are a Scala, Java, or Python developer with an interest in machine learning and data analysis and are eager to learn how to apply common machine learning techniques at scale using the Spark framework, this is the book for you.

  • Handbook of Research on Applications and Implementations of Machine Learning Techniques
    Handbook of Research on Applications and Implementations of Machine Learning Techniques
    By Sathiyamoorthi Velayutham

    "This book examines the practical applications and implementation of various machine learning techniques in various fields such as agriculture, medical, image processing, and networking"--

  • Learn R for Applied Statistics: With Data Visualizations, Regressions, and Statistics
    Learn R for Applied Statistics: With Data Visualizations, Regressions, and Statistics
    By ERIC GOH MING HUI. HUI

    What You Will Learn Discover R, statistics, data science, data mining, and big data Master the fundamentals of R programming, including variables and arithmetic, vectors, lists, data frames, conditional statements, loops, and functions Work ...

  • Machine Learning Under a Modern Optimization Lens
    By Dimitris Bertsimas, Jack William Dunn

    "The book provides an original treatment of machine learning (ML) using convex, robust and mixed integer optimization that leads to solutions to central ML problems at large scale that can be found in seconds/minutes, can be certified to be ...

  • Fundamentals of Machine Learning
    Fundamentals of Machine Learning
    By Thomas Trappenberg

    This is followed by two more chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society.

  • Non-convex Optimization for Machine Learning
    Non-convex Optimization for Machine Learning
    By Prateek Jain, Purushottam Kar

    Non-convex Optimization for Machine Learning takes an in-depth look at the basics of non-convex optimization with applications to machine learning.