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

Sunila Gollapudi

$9.99 ~~$40.99~~

3.9 (19 Ratings)

eBook Jan 2016 468 pages 1st Edition

Sunila Gollapudi

$9.99 ~~$40.99~~

3.9 (19 Ratings)

eBook Jan 2016 468 pages 1st Edition

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

View table of contents

Preview Book

Practical Machine Learning

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...

Big data and the context of large-scale Machine learning

I have covered some of the core aspects of big data in my previous Packt book titled Getting Started with Greenplum for Big Data Analytics. In this section, we will quickly recap some of the core aspects of big data and its impact in the field of Machine learning:

The definition of large-scale is a scale of terabytes, petabytes, exabytes, or higher. This is typically the volume that cannot be handled by traditional database engines. The following chart lists the orders of magnitude that represents data volumes:

Multiples of bytes
SI decimal prefixes		Binary Usage
Name(Symbol)	Value	Binary Usage
Kilobyte (KB)	103	210
Megabyte (MB)	106	220
Gigabyte (GB)	109	230
Terabyte (TB)	1012	240
Petabyte (PB)	1015	250
Exabyte (EB)	1018	260
Zettabyte (ZB)	1021	270
Yottabyte (YB)	1024	280

Data formats that are referred to in this context are distinct; they are generated and consumed, and need not be structured (for example,...

Download Code

Key benefits

Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark
Comprehensive practical solutions taking you into the future of machine learning
Go a step further and integrate your machine learning projects with Hadoop

Description

This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and 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. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application. With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data. You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naïve Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements in machine learning, with worked examples and guidance on deep learning and reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced machine learning methodologies.

Who is this book for?

This book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately.

What you will learn

Implement a wide range of algorithms and techniques for tackling complex data
Get to grips with some of the most powerful languages in data science, including R, Python, and Julia
Harness the capabilities of Spark and Hadoop to manage and process data successfully
Apply the appropriate machine learning technique to address real-world problems
Get acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learning
Explore the future of machine learning and dive deeper into polyglot persistence, semantic data, and more

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

$50.99

$48.99

Total $ 99.98

Filter reviews by

All

Amazon verified reviews

Amazon buyer Sep 21, 2018

Very informative.

Amazon Verified review

Santosh Korrapati Dec 19, 2017

This is Superhuman effort to explain machine learrning so lucidly and interestingly. I simply loved it.

Grypho Mar 01, 2016

This book covers most of the basic concepts and a wide range of technologies and methods in the field of Machine Learning. Theory is This book covers most of the basic concepts and a wide range of technologies and methods in the field of Machine Learning. Theory is explained clearly but it's far too synthetic to be considered a textbook; instead it's perfect for a quick rehearsal of theory or just an introduction to the ML algorithms and tools. As implied in the "Practical" title, the code examples are not optional but should be considered as integral part of the book: they are clear (although sometimes too naive for real application) and reasonably well commented. To get the most out of this book the reader should already have a basic knowledge of machine learning concepts and possibly some confidence with the technology she/he wants to use (Java/Scala/Python/Julia/R...).DISCLOSURE: I received a complimentary copy by the editor in order to read it and write my genuine and sincere review

Brad Aug 14, 2017

This is a really outstanding book that gives you an explanation of what machine learning is in the most direct way. So many machine learning books are bogged down in boring dry theory that really doesnt give you an intuitive and high level view of what you are doing. This book cuts through the clutter and explains in very practical terms what you are trying to do. The other reviews criticize the command of english of the author, I did not find anything that was vague or hard to understand and only came across a few minor errors in word choice that would indicate a foriegn speaker. Also the other reviewers criticized the coverage of so many different tools and programming langauges. All I can say is that this is a great addition to this book that all the major machine learning tools are covered...including Hadoop! Most machine learning books just use R because that is what has been used in statistics for years so there is a lot of support for it. But R is very limiting and obsolete. Python is currently being used as a stopgap replacement for R because Python is a very intuitive and versitile scripting language, but the Python is really not made for statistics or machine learning and has no built-in support for it and requires libraries like Scikit to function. Julia is a new language that is expressly designed for data processing that will eventually take over machine learning and eliminate Python and R. So all of these languages are covered in the book so this book will prepare someone do deal with all the technologies currently in use to process machine learning data. No other machine learning book that I am aware of can say that. I do have a criticism for this book, I do wish the author went into a little more detail in describing the machine learning algorithms, by using diagrams and more practical examples. I was almost tempted to give this book 4 stars for this lacking, but overall the book is so far ahead of all the other machine learning books out there that I have to give it 5 stars.

Ravi Mar 09, 2016

I was the technical reviewer for this book. This book will help the readers in understanding machine learning, implement algorithms and also play around with technology stack using R, Python, Mahout, Hadoop. It covers major aspects in practical machine learning techniques with real-world examples across multiple verticals. This book will immensely help a rookie to be a star in machine learning and can also compete in kaggle competition. For the ones who are good at machine learning, this book will expose you to the multiple approaches you can use to solve a problem.

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

What do you get with eBook?

Practical Machine Learning

Chapter 2. Machine learning and Large-scale datasets

Big data and the context of large-scale Machine learning

Algorithms and Concurrency

Technology and implementation options for scaling-up Machine learning

MapReduce programming paradigm

Summary

Page 1 of 5

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with eBook?

Product Details

Frequently bought together

Table of Contents

Recommendations for you

Customer reviews

Filter reviews by

People who bought this also bought

About the author

FAQs

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

What do you get with eBook?

Contact Details

Billing Address

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with eBook?

Contact Details

Billing Address

Product Details

Packt Subscriptions

Frequently bought together

Table of Contents

Recommendations for you

Customer reviews

Filter reviews by

People who bought this also bought

About the author

FAQs