Essential Resource for modern data scientist - Practical Machine Learning

June 13, 2017, 8:19 p.m. By: Vishakha Jha

Practical Machine Learning

This book elaborates Machine learning techniques, providing hidden tips and tricks for several types of data using practical real-world examples. While Machine learning can be highly theoretical this book also explains the underlying principles. The book is written by Sunila Gollapudi who works as Vice President Technology with Broadridge Financial Solutions (India) Pvt. Ltd. and owns subsidiary of the US-based Broadridge Financial Solutions Inc. (BR). This book has been written for data scientists who want to see Machine learning in action and explore its real-world applications.

Machine learning is a method of data analysis through an automated analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.

This book provides you with fully-coded working examples using a wide range of machine learning libraries and tools along with practical solutions taking you into the future of machine learning and will make you implement a whole suite of open source tools, frameworks, and languages in machine learning. It explains the fundamentals of machine learning but describes the complexities of real world data before moving on to using Hadoop and its wider ecosystem.

It demonstrates the implementation of a wide range of algorithms and techniques for tackling complex data and introduces some of the most powerful languages in data science, including R, Python, and Julia. Book teaches you to apply the appropriate machine learning technique to address real-world problems and acquaints you with deep learning to find out how neural networks are being used at the cutting-edge of machine learning. It will provide you with the knowledge of different machine learning techniques for both supervised and unsupervised learning. The book also explains about the advancements in machine learning along with examples and to provide you with practical demonstration and samples.

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