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Ramasubramanian K., Singh A. - Machine Learning Using R [2017, PDF, ENG]

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Post 09-Jan-2017 01:00

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Machine Learning Using R
Год издания: 2017
Автор: Ramasubramanian K., Singh A.
Жанр или тематика: Программирование
Издательство: Apress
ISBN: 978-1484223338
Язык: Английский
Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 566
Описание: This book is inspired by the Machine Learning Model Building Process Flow, which provides the reader the ability to understand a ML algorithm and apply the entire process of building a ML model from the raw data.
This new paradigm of teaching Machine Learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in Blockchain and Capitalism makes it easy for someone to connect the dots.
For every Machine Learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R.
All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. In the end, readers will learn some of the latest technological advancements in building a scalable machine learning model with Big Data.
Who This Book is For:
Data scientists, data science professionals and researchers in academia who want to understand the nuances of Machine learning approaches/algorithms along with ways to see them in practice using R. The book will also benefit the readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig and Spark.
What you will learn:
1. ML model building process flow
2. Theoretical aspects of Machine Learning
3. Industry based Case-Study
4. Example based understanding of ML algorithm using R
5. Building ML models using Apache Hadoop and Spark

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Оглавление

Contents at a Glance
About the Authors ........................................................................... xix
About the Technical Reviewer ........................................................ xxi
Acknowledgments ........................................................................ xxiii
Chapter 1: Introduction to Machine Learning and R ....................... 1
Chapter 2: Data Preparation and Exploration ............................... 31
Chapter 3: Sampling and Resampling Techniques ....................... 67
Chapter 4: Data Visualization in R .............................................. 129
Chapter 5: Feature Engineering .................................................. 181
Chapter 6: Machine Learning Theory and Practices ................... 219
Chapter 7: Machine Learning Model Evaluation ......................... 425
Chapter 8: Model Performance Improvement ............................. 465
Chapter 9: Scalable Machine Learning and Related
Technologies ............................................................................... 519
Index .............................................................................................. 555
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