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R Machine Learning Projects

You're reading from  R Machine Learning Projects

Product type Book
Published in Jan 2019
Publisher Packt
ISBN-13 9781789807943
Pages 334 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Dr. Sunil Kumar Chinnamgari Dr. Sunil Kumar Chinnamgari
Profile icon Dr. Sunil Kumar Chinnamgari
LinkedIn
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.
See other products by Dr. Sunil Kumar Chinnamgari

Table of Contents (12) Chapters

Preface 1. Exploring the Machine Learning Landscape 2. Predicting Employee 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 Networks 7. Credit Card Fraud Detection Using Autoencoders 8. Automatic Prose Generation with Recurrent Neural Networks 9. Winning the Casino Slot Machines with Reinforcement Learning 10. The Road Ahead
11. Other Books You May Enjoy

References

While the recommenderlab library is super popular in the R community, this is not the only choice for building a recommendation system. Here are some other popular libraries you may rely on to implement recommendation engines:

  • rrecsys: There are several popular recommendation systems, such as Global/Item/User-Average baselines, Item-Based KNN, FunkSVD, BPR, and weighted ALS for rapid prototyping. Refer to https://cran.r-project.org/web/packages/rrecsys/index.htmlImplementations for more information.
  • recosystem: The R wrapper of the libmf library (http://www.csie.ntu.edu.tw/~cjlin/libmf/) for recommender system using matrix factorization. It is typically used to approximate an incomplete matrix using the product of two matrices in a latent space. Other common names for this task include collaborative filtering, matrix completion, and matrix recovery. High-performance...
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Authors (1)

Profile icon Dr. Sunil Kumar Chinnamgari
LinkedIn
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.
See other products by Dr. Sunil Kumar Chinnamgari

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