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

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
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 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.

Table of Contents
Preparing and Understanding Data
Linear Regression
Logistic Regression
Advanced Feature Selection in Linear Models
K-Nearest Neighbors and Support Vector Machines
Tree-Based Classification
Neural Networks and Deep Learning
Creating Ensembles and Multiclass Methods
Cluster Analysis
Principal Component Analysis
Association Analysis
Time Series and Causality
Text Mining
Exploring the Machine Learning Landscape
Predicting Employee Attrition Using Ensemble Models
Implementing a Joke Recommendation Engine
Sentiment Analysis of Amazon Reviews with NLP
Customer Segmentation Using Wholesale Data
Image Recognition Using Deep Neural Networks
Credit Card Fraud Detection Using Autoencoders
Automatic Prose Generation with Recurrent Neural Networks
Winning the Casino Slot Machines with Reinforcement Learning