Advanced Machine Learning with R: Tackle data analytics and machine learning challenges and build complex applications with R 3.5

Advanced Machine Learning with R: Tackle data analytics and machine learning challenges and build complex applications with R 3.5Reviews
Author: Cory Lesmeister
Pub Date: 2019
ISBN: 978-1838641771
Pages: 664
Language: English
Format: EPUB
Size: 58 Mb

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Master machine learning techniques with real-world projects that interface TensorFlow with R, H2O, MXNet, and other languages
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 tackle realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. You’ll explore different clustering techniques to segment customers using wholesale data and use TensorFlow and Keras-R for performing advanced computations. You’ll also be introduced to reinforcement learning along with its various use cases and models. Additionally, it shows you 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.
This Learning Path includes content from the following Packt products:

  • R Machine Learning Projects by Dr. Sunil Kumar Chinnamgari
  • Mastering Machine Learning with R – Third Edition by Cory Lesmeister

What you will learn

  • Develop a joke recommendation engine to recommend 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 machine using reinforcement learning
  • Implement 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