This is the code repository for Practical Machine Learning, published by Packt.
Tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques
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 book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
using PyPlot
x = linspace(-2pi, 2pi)
y = sin(x)
plot(x, y, "--b")
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-14).
| Chapter | Software required | OS required |
|---|---|---|
| 3 | Hadoop 2.x | Windows, Mac OS X, and Linux (Any) |
| 4-13 | R (2.15.1), Apache Mahout (0.9), Python(sckit-learn), Julia(0.3.4) and Apache Spark (with Scala 2.10.4) | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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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