R machine learning projects :: implement supervised, unsupervised, and reinforcement learning techniques using R 3.5 /
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 do...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2019.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 1789806097 9781789806090 |
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| spelling | Chinnamgari, Sunil Kumar, author. R machine learning projects : implement supervised, unsupervised, and reinforcement learning techniques using R 3.5 / Sunil Kumar Chinnamgari. Birmingham, UK : Packt Publishing, 2019. 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from title page (Safari, viewed March 6, 2019). Includes bibliographical references. 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. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Apprentissage automatique. R (Langage de programmation) Machine learning fast R (Computer program language) fast has work: R Machine Learning Projects (Text) https://id.oclc.org/worldcat/entity/E39PCXJTJkRk33fx7j7KT8TBmq https://id.oclc.org/worldcat/ontology/hasWork FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2000711 Volltext |
| spellingShingle | Chinnamgari, Sunil Kumar R machine learning projects : implement supervised, unsupervised, and reinforcement learning techniques using R 3.5 / Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Apprentissage automatique. R (Langage de programmation) Machine learning fast R (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh2002004407 |
| title | R machine learning projects : implement supervised, unsupervised, and reinforcement learning techniques using R 3.5 / |
| title_auth | R machine learning projects : implement supervised, unsupervised, and reinforcement learning techniques using R 3.5 / |
| title_exact_search | R machine learning projects : implement supervised, unsupervised, and reinforcement learning techniques using R 3.5 / |
| title_full | R machine learning projects : implement supervised, unsupervised, and reinforcement learning techniques using R 3.5 / Sunil Kumar Chinnamgari. |
| title_fullStr | R machine learning projects : implement supervised, unsupervised, and reinforcement learning techniques using R 3.5 / Sunil Kumar Chinnamgari. |
| title_full_unstemmed | R machine learning projects : implement supervised, unsupervised, and reinforcement learning techniques using R 3.5 / Sunil Kumar Chinnamgari. |
| title_short | R machine learning projects : |
| title_sort | r machine learning projects implement supervised unsupervised and reinforcement learning techniques using r 3 5 |
| title_sub | implement supervised, unsupervised, and reinforcement learning techniques using R 3.5 / |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Apprentissage automatique. R (Langage de programmation) Machine learning fast R (Computer program language) fast |
| topic_facet | Machine learning. R (Computer program language) Apprentissage automatique. R (Langage de programmation) Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2000711 |
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