Machine Learning with Spark

Machine Learning with Spark
ISBN-10
1783288515
ISBN-13
9781783288519
Category
Machine learning
Language
English
Published
2015-02-20
Author
Nick Pentreath

Description

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. While it may be useful to have a basic understanding of Spark, no previous experience is required.

Other editions

Similar books

  • 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
    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
    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
    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
    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
    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
    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
    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.