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Machine Learning with R Quick Start Guide - A beginner's guide to implementing machine learning techniques from scratch using R 3.5 (Paperback)
Loot Price: R850
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Machine Learning with R Quick Start Guide - A beginner's guide to implementing machine learning techniques from scratch using R 3.5 (Paperback)
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Learn how to use R to apply powerful machine learning methods and
gain insight into real-world applications using clustering,
logistic regressions, random forests, support vector machine, and
more. Key Features Use R 3.5 to implement real-world examples in
machine learning Implement key machine learning algorithms to
understand the working mechanism of smart models Create end-to-end
machine learning pipelines using modern libraries from the R
ecosystem Book DescriptionMachine Learning with R Quick Start Guide
takes you on a data-driven journey that starts with the very basics
of R and machine learning. It gradually builds upon core concepts
so you can handle the varied complexities of data and understand
each stage of the machine learning pipeline. From data collection
to implementing Natural Language Processing (NLP), this book covers
it all. You will implement key machine learning algorithms to
understand how they are used to build smart models. You will cover
tasks such as clustering, logistic regressions, random forests,
support vector machines, and more. Furthermore, you will also look
at more advanced aspects such as training neural networks and topic
modeling. By the end of the book, you will be able to apply the
concepts of machine learning, deal with data-related problems, and
solve them using the powerful yet simple language that is R. What
you will learn Introduce yourself to the basics of machine learning
with R 3.5 Get to grips with R techniques for cleaning and
preparing your data for analysis and visualize your results Learn
to build predictive models with the help of various machine
learning techniques Use R to visualize data spread across multiple
dimensions and extract useful features Use interactive data
analysis with R to get insights into data Implement supervised and
unsupervised learning, and NLP using R libraries Who this book is
forThis book is for graduate students, aspiring data scientists,
and data analysts who wish to enter the field of machine learning
and are looking to implement machine learning techniques and
methodologies from scratch using R 3.5. A working knowledge of the
R programming language is expected.
General
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