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Master the craft of predictive modeling in R by developing
strategy, intuition, and a solid foundation in essential concepts
About This Book * Grasping the major methods of predictive modeling
and moving beyond black box thinking to a deeper level of
understanding * Leveraging the flexibility and modularity of R to
experiment with a range of different techniques and data types *
Packed with practical advice and tips explaining important concepts
and best practices to help you understand quickly and easily Who
This Book Is For Although budding data scientists, predictive
modelers, or quantitative analysts with only basic exposure to R
and statistics will find this book to be useful, the experienced
data scientist professional wishing to attain master level status ,
will also find this book extremely valuable.. This book assumes
familiarity with the fundamentals of R, such as the main data
types, simple functions, and how to move data around. Although no
prior experience with machine learning or predictive modeling is
required, there are some advanced topics provided that will require
more than novice exposure. What You Will Learn * Master the steps
involved in the predictive modeling process * Grow your expertise
in using R and its diverse range of packages * Learn how to
classify predictive models and distinguish which models are
suitable for a particular problem * Understand steps for tidying
data and improving the performing metrics * Recognize the
assumptions, strengths, and weaknesses of a predictive model *
Understand how and why each predictive model works in R * Select
appropriate metrics to assess the performance of different types of
predictive model * Explore word embedding and recurrent neural
networks in R * Train models in R that can work on very large
datasets In Detail R offers a free and open source environment that
is perfect for both learning and deploying predictive modeling
solutions. With its constantly growing community and plethora of
packages, R offers the functionality to deal with a truly vast
array of problems. The book begins with a dedicated chapter on the
language of models and the predictive modeling process. You will
understand the learning curve and the process of tidying data. Each
subsequent chapter tackles a particular type of model, such as
neural networks, and focuses on the three important questions of
how the model works, how to use R to train it, and how to measure
and assess its performance using real-world datasets. How do you
train models that can handle really large datasets? This book will
also show you just that. Finally, you will tackle the really
important topic of deep learning by implementing applications on
word embedding and recurrent neural networks. By the end of this
book, you will have explored and tested the most popular modeling
techniques in use on real- world datasets and mastered a diverse
range of techniques in predictive analytics using R. Style and
approach This book takes a step-by-step approach in explaining the
intermediate to advanced concepts in predictive analytics. Every
concept is explained in depth, supplemented with practical examples
applicable in a real-world setting.
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