Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Practical Machine Learning

You're reading from   Practical Machine Learning Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book – packed with practical tutorials

Arrow left icon
Product type Paperback
Published in Jan 2016
Publisher Packt
ISBN-13 9781784399689
Length 468 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Sunila Gollapudi Sunila Gollapudi
Author Profile Icon Sunila Gollapudi
Sunila Gollapudi
Arrow right icon
View More author details
Toc

Table of Contents (16) Chapters Close

Preface 1. Introduction to Machine learning FREE CHAPTER 2. Machine learning and Large-scale datasets 3. An Introduction to Hadoop's Architecture and Ecosystem 4. Machine Learning Tools, Libraries, and Frameworks 5. Decision Tree based learning 6. Instance and Kernel Methods Based Learning 7. Association Rules based learning 8. Clustering based learning 9. Bayesian learning 10. Regression based learning 11. Deep learning 12. Reinforcement learning 13. Ensemble learning 14. New generation data architectures for Machine learning Index

Chapter 2. Machine learning and Large-scale datasets

We have seen a dramatic change in the way data has been handled in the recent past with the advent of big data. The field of Machine learning has seen the need to include scaling up strategies to handle the new age data requirements. This actually means that some of the traditional Machine learning implementations will not all be relevant in the context of big data now. Infrastructure and tuning requirements are now the challenges with the need to store and process large scale data complimented by the data format complexities.

With the evolution of hardware architectures, accessibility of cheaper hardware with distributed architectures and new programming paradigms for simplified parallel processing options, which can now be applied to many learning algorithms, we see a rising interest in scaling up the Machine learning systems.

The topics listed next are covered in-depth in this chapter:

  • An introduction to big data and typical...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image