Advanced Machine Learning with R

Book Description

Master machine learning techniques with real-world that TensorFlow with R, H2O, MXNet, and other languages

Key Features

  • Gain expertise in machine learning, deep learning and other techniques
  • Build intelligent end-to-end projects for finance, , and a variety of domains
  • Implement multi-class classification, regression, and clustering

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 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
  • 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

Who this book is for

If you are a data analyst, data scientist, or machine learning developer this is an ideal Learning Path for you. Each 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.

Table of Contents

  1. Preparing and Understanding Data
  2. Linear Regression
  3. Logistic Regression
  4. Advanced Feature Selection in Linear Models
  5. K-Nearest Neighbors and Support Vector Machines
  6. Tree-Based Classification
  7. Neural Networks and Deep Learning
  8. Creating Ensembles and Multiclass Methods
  9. Cluster Analysis
  10. Principal Component Analysis
  11. Association Analysis
  12. Time Series and Causality
  13. Text Mining
  14. Exploring the Machine Learning
  15. Predicting Employee Attrition Using Ensemble Models
  16. Implementing a Joke Recommendation Engine
  17. Sentiment Analysis of Amazon Reviews with NLP
  18. Customer Segmentation Using Wholesale Data
  19. Image Recognition Using Deep Neural Networks
  20. Credit Card Fraud Detection Using Autoencoders
  21. Automatic Prose Generation with Recurrent Neural Networks
  22. Winning the Casino Slot Machines with Reinforcement Learning

Book Details