E-Books →R Machine Learning Projects: Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5 (TRUE PDF)
English | 2019 | ISBN: 1789807948 | 325 Pages | True PDF/ePUB | 19 MB
R is one of the most popular languages when it comes to perfog computational statistics (statistical computing) easily and exploring the mathematical side of machine learning.
With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization.
This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you'll implement a joke recommendation ee and learn how to perform sennt analysis on reviews. You'll also explore different clustering techniques to snt customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine.
By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.
What you will learn
Explore deep neural networks and various frameworks that can be used in R
Develop a joke recommendation ee to recommend jokes that match users' tastes
Create powerful ML models with ensembles to predict employee attrition
Build autoencoders for credit card fraud detection
Work with image recognition and convolutional neural networks
Make predictions for casino slot machine using reinforcement learning
Implement NLP techniques for sennt analysis and customer sntation
Who this book is for
If you're a data analyst, data scientist, or machine learning developer who wants to master machine learning concepts using R by building real-world projects, this is the book for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this book.
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