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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

Product type Paperback

Published in Jan 2016

Publisher Packt

ISBN-13 9781784399689

Length 468 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Machine Learning

Author (1):

Sunila Gollapudi

View More author details

Table of Contents (16) Chapters

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

Emerging perspectives & drivers for new age data architectures

Driver 1—BIG data intervention.

We have defined big data and large dataset concepts in Chapter 2, Machine learning and Large-scale datasets. The data that is now being ingested and needs to be processed typically has the following characteristics:

Source: Depending upon the nature of the information, the source may be a real-time stream of data (for example, trade transactions), or batches of data containing updates since the last sync
Content: The data may represent different types of information. Often, this information is related to other pieces of data and is needed to be connected
The following screenshot shows the types of data and different sources that need to be supported:
Volume: Depending upon the nature of the data, the volumes that are being processed may vary. For example, master data or the securities definition data are relatively fixed, whereas the transaction data is enormous compared to the other two...

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Authors (1)

Sunila Gollapudi

Sunila Gollapudi

Sunila Gollapudi works as Vice President Technology with Broadridge Financial Solutions (India) Pvt. Ltd., a wholly owned subsidiary of the US-based Broadridge Financial Solutions Inc. (BR). She has close to 14 years of rich hands-on experience in the IT services space. She currently runs the Architecture Center of Excellence from India and plays a key role in the big data and data science initiatives. Prior to joining Broadridge she held key positions at leading global organizations and specializes in Java, distributed architecture, big data technologies, advanced analytics, Machine learning, semantic technologies, and data integration tools. Sunila represents Broadridge in global technology leadership and innovation forums, the most recent being at IEEE for her work on semantic technologies and its role in business data lakes. Sunila's signature strength is her ability to stay connected with ever changing global technology landscape where new technologies mushroom rapidly, connect the dots and architect practical solutions for business delivery. A post graduate in computer science, her first publication was on Big Data Datawarehouse solution, Greenplum titled Getting Started with Greenplum for Big Data Analytics, Packt Publishing. She's a noted Indian classical dancer at both national and international levels, a painting artist, in addition to being a mother, and a wife.

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