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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

Julia

Julia, in recent times, has gained much popularity and adoption in the Machine learning and data science fields as a high-performance alternative to Python. Julia is a dynamic programming language that is built to support distributed and parallel computing, thus known to be convenient and fast.

Performance in Julia is a result of the JIT compiler and type interfacing feature. Also, unlike other numeric programming languages, Julia does not enforce vectorization of values. Similar to R, MATLAB, and Python, Julia provides ease and expressiveness for high-level numerical computing.

Following are some key characteristics of Julia:

The core APIs and mathematical primitive operations are written in Julia
It consists rich types for constructing and describing objects
Julia supports for multiple dispatch that enable using functions across many combinations of arguments
It facilitates the automation of specialized code generation for different argument types
Proven performance is on par with statically...

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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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