Fr. 171.60

R - Unleash Machine Learning Techniques: Smarter data analytics

English · Paperback / Softback

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Description

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Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner.

Key Features:Build your confidence with R and find out how to solve a huge range of data-related problems
Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today
Don't just learn - apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysis

Book Description:
R is the established language of data analysts and statisticians around the world. And you shouldn't be afraid to use it...

This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R.

In the first module you'll get to grips with the fundamentals of R. This means you'll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems.

For the following two modules we'll begin to investigate machine learning algorithms in more detail. To build upon the basics, you'll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they're all focused on solving real problems in different areas, ranging from finance to social media.

This Learning Path has been curated from three Packt products:
¿ R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar
¿ Machine Learning with R - Second Edition By Brett Lantz
¿ Mastering Machine Learning with R By Cory Lesmeister

What You Will Learn:Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action
Solve interesting real-world problems using machine learning and R as the journey unfolds
Write reusable code and build complete machine learning systems from the ground up
Learn specialized machine learning techniques for text mining, social network data, big data, and more
Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
Evaluate and improve the performance of machine learning models
Learn specialized machine learning techniques for text mining, social network data, big data, and more

Who this book is for:
Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science

About the author










Raghav Bali is a Staff Data Scientist at Delivery Hero, a leading food delivery service headquartered in Berlin, Germany. With 12+ years of expertise, he specializes in research and development of enterprise-level solutions leveraging Machine Learning, Deep Learning, Natural Language Processing, and Recommendation Engines for practical business applications. Besides his professional endeavors, Raghav is an esteemed mentor and an accomplished public speaker. He has contributed to multiple peer-reviewed papers and authored multiple well received books. Additionally, he holds co-inventor credits on multiple patents in healthcare, machine learning, deep learning, and natural language processing.

Product details

Authors Raghav Bali, Brett Lantz, Dipanjan Sarkar
Publisher Packt Publishing
 
Languages English
Product format Paperback / Softback
Released 19.06.2017
 
EAN 9781787127340
ISBN 978-1-78712-734-0
No. of pages 1140
Dimensions 191 mm x 235 mm x 61 mm
Weight 1915 g
Subject Natural sciences, medicine, IT, technology > IT, data processing > Programming languages

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