Practical Machine Learning


Practical Machine Learning by Sunila Gollapudi
English | Jan. 30, 2016 | ISBN: 178439968X | 468 Pages | AZW3/MOBI/EPUB/PDF (conv) | 76.39 MB

This book has been created for data scientists who want to see Machine learning in action and explore its real-world applications. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately.

About This Book

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

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 Mahout used in conjunction with Hadoop to manage and process data successfully
Apply the appropriate Machine learning technique to address a real-world problem
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
In Detail

This book explores an extensive range of Machine learning techniques, uncovering hidden tips and tricks for several types of data using practical 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.

We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for modern data scientists who want to get to grips with Machine learning's real-world application.

The book also explores cutting-edge advances 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.

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