R Machine Learning Projects: Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5 - Couverture souple

Chinnamgari, Dr. Sunil Kumar

Couverture souple

ISBN 10 : 1789807948 ISBN 13 : 9781789807943

Editeur : Packt Publishing, 2019

Synopsis

Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more

Key Features

• Master machine learning, deep learning, and predictive modeling concepts in R 3.5
• Build intelligent end-to-end projects for finance, retail, social media, and a variety of domains
• Implement smart cognitive models with helpful tips and best practices

Book Description

R is one of the most popular languages when it comes to performing 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 engine and learn how to perform sentiment analysis on Amazon reviews. You'll also explore different clustering techniques to segment 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 engine 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 sentiment analysis and customer segmentation

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.

Table of Contents

1. Exploring the Machine Learning Landscape
2. Predicting Employees Attrition using Ensemble models
3. Implementing a Jokes Recommendation Engine
4. Sentiment Analysis of Amazon Reviews with NLP
5. Customer Segmentation Using Wholesale Data
6. Image Recognition using Deep Neural Network
7. Credit Card Fraud Detection Using Autoencoders
8. Automatic Prose Generation with Recurrent Neural Networks
9. Winning the Casino Slot Machine with Reinforcement Learning
10. Appendix

A propos du livre

À propos de l?auteur

Dr. Sunil Kumar Chinnamgari has a Ph.D. in computer science and specializes in machine learning and natural language processing. He is an AI researcher with more than 14 years of industry experience. Currently, he works in the capacity of lead data scientist with a US financial giant. He has published several research papers in Scopus and IEEE journals and is a frequent speaker at various meetups. He is an avid coder and has won multiple hackathons. In his spare time, Sunil likes to teach, travel, and spend time with family.

Détails de l'édition

• Éditeur: Packt Publishing
• Date d'édition: 2019
• Langue: anglais
• ISBN 10: 1789807948
• ISBN 13: 9781789807943
• Reliure: Broché
• Nombre de pages: 334
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible