Amazon cover image
Image from Amazon.com

Practical machine learning / Sunila Gollapudi

By: Material type: TextTextPublication details: Birmingham, UK : Packt Publishing, Limited, c2016Description: xvi, 433 pages : illustrations ; 24 cmISBN:
  • 9781784399689
Subject(s): LOC classification:
  • QA 76.9 .G65 2016
Contents:
Chapter 1. Introduction to machine learning -- Chapter 2. Machine learning and large-scale datasets -- Chapter 3. An introduction to Hadoop's architecture and ecosystem -- Chapter 4. Machine learning tools, libraries, and frameworks -- Chapter 5. Decision tree based learning -- Chapter 6. Instance and Kernel methods based learning -- Chapter 7. Association rules based learning -- Chapter 8. Clustering based learning -- Chapter 9. Bayesian learning -- Chapter 10 : Regression based learning -- Chapter 11. Deep learning -- Chapter 12. Reinforcement learning -- Chapter 13. Ensemble learning -- Chapter 14. New generation data architectures for machine learning.
Summary: This book explores an extensive range of machine learning techniques, uncovering hidden tips and tricks for several types of data using practical real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles.
Item type: Books
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Home library Collection Call number Copy number Status Date due Barcode
Books Books National University - Manila LRC - Graduate Studies General Circulation General Education GC QA 76.9 .G65 2016 (Browse shelf(Opens below)) c.1 Available NULIB000013703

Includes index.

Chapter 1. Introduction to machine learning -- Chapter 2. Machine learning and large-scale datasets -- Chapter 3. An introduction to Hadoop's architecture and ecosystem -- Chapter 4. Machine learning tools, libraries, and frameworks -- Chapter 5. Decision tree based learning -- Chapter 6. Instance and Kernel methods based learning -- Chapter 7. Association rules based learning -- Chapter 8. Clustering based learning -- Chapter 9. Bayesian learning -- Chapter 10 : Regression based learning -- Chapter 11. Deep learning -- Chapter 12. Reinforcement learning -- Chapter 13. Ensemble learning -- Chapter 14. New generation data architectures for machine learning.

This book explores an extensive range of machine learning techniques, uncovering hidden tips and tricks for several types of data using practical real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles.

There are no comments on this title.

to post a comment.