Advanced Machine Learning with R : Tackle data analytics and machine learning challenges and build complex applications with R 3.5
Language: English
Published by Packt Publishing, 2019
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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£ 77.28
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - Master an array of machine learning techniques with real-world projects that interface TensorFlow with R, H2O, MXNet, and other languagesKey Features: Gain expertise in machine learning, deep learning, and predictive modeling techniques Build intelligent end-to-end projects for finance, social media, and a variety of other domains Implement multi-class classification, regression, and clustering in your modelsBook Description:R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics.This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You'll work through realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. Next, you'll explore different clustering techniques to segment customers using wholesale data and even apply TensorFlow and Keras-R for performing advanced computations. Each chapter will help you implement advanced machine learning algorithms using real-world examples. You'll also be introduced to reinforcement learning along with its use cases and models. Finally, this Learning Path will provide you with a glimpse into how some of these black box models can be diagnosed and understood.By the end of this Learning Path, you'll be equipped with the skills you need to deploy machine learning techniques in your own projects.What You Will Learn: Develop a joke recommendation engine to show jokes that match users' tastes Build autoencoders for credit card fraud detection Work with image recognition and convolutional neural networks Make predictions for casino slot machines using reinforcement learning Implement natural language processing (NLP) techniques for sentiment analysis and customer segmentation Produce simple and effective data visualizations for improved insights Use NLP to extract insights for text Implement tree-based classifiers including random forest and boosted treeWho this book is for:If you're a data analyst, data scientist, or machine learning developer who wants to master machine learning techniques using R, this is an ideal Learning Path 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 Learning Path.…
Seller Inventory # 9781838641771
- Title
- Advanced Machine Learning with R : Tackle data analytics and machine learning challenges and build complex applications with R 3.5
- Author
- Sunil Kumar Chinnamgari
- Publisher
- Packt Publishing
- Publication year
- 2019
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1838641777
- ISBN 13
- 9781838641771
- Item weight
- 1,219 grams
- Dimensions
- 235x191x35 mm
Master an array of machine learning techniques with real-world projects that interface TensorFlow with R, H2O, MXNet, and other languages
Key Features:
- Gain expertise in machine learning, deep learning, and predictive modeling techniques
- Build intelligent end-to-end projects for finance, social media, and a variety of other domains
- Implement multi-class classification, regression, and clustering in your models
Book Description:
R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics.
This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You'll work through realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. Next, you'll explore different clustering techniques to segment customers using wholesale data and even apply TensorFlow and Keras-R for performing advanced computations. Each chapter will help you implement advanced machine learning algorithms using real-world examples. You'll also be introduced to reinforcement learning along with its use cases and models. Finally, this Learning Path will provide you with a glimpse into how some of these black box models can be diagnosed and understood.
By the end of this Learning Path, you'll be equipped with the skills you need to deploy machine learning techniques in your own projects.
What You Will Learn:
- Develop a joke recommendation engine to show jokes that match users' tastes
- Build autoencoders for credit card fraud detection
- Work with image recognition and convolutional neural networks
- Make predictions for casino slot machines using reinforcement learning
- Implement natural language processing (NLP) techniques for sentiment analysis and customer segmentation
- Produce simple and effective data visualizations for improved insights
- Use NLP to extract insights for text
- Implement tree-based classifiers including random forest and boosted tree
Who this book is for:
If you're a data analyst, data scientist, or machine learning developer who wants to master machine learning techniques using R, this is an ideal Learning Path 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 Learning Path.
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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