R Machine Learning Projects

Name: R Machine Learning Projects | Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5
Brand: Packt Publishing
Price: 44.59 EUR
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Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5

Chinnamgari Sunil Kumar Chinnamgari(Autor*in)

Packt Publishing

1. Auflage

Erschienen am 14. Januar 2019

334 Seiten

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978-1-78980-609-0 (ISBN)

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Beschreibung

Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more

Key Features

Master machine learning, deep learning, and predictive modeling concepts in R 3.5
Build intelligent end-to-end projects for finance, retail, social media, and a variety of domains
Implement smart cognitive models with helpful tips and best practices

Book Description

R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization.

This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you'll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You'll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine.

By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.

What you will learn

Explore deep neural networks and various frameworks that can be used in R
Develop a joke recommendation engine to recommend jokes that match users' tastes
Create powerful ML models with ensembles to predict employee attrition
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

Who this book is for

If you're a data analyst, data scientist, or machine learning developer who wants to master machine learning concepts using R by building real-world projects, this is the book 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 book.

Weitere Details

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Inhalt

Cover
Title Page
Copyright and Credits
About Packt
Dedication
Contributors
Table of Contents
Preface
Chapter 1: Exploring the Machine Learning Landscape
ML versus software engineering
Types of ML methods
Supervised learning
Unsupervised learning
Semi-supervised learning
Reinforcement learning
Transfer learning
ML terminology - a quick review
Deep learning
Big data
Natural language processing
Computer vision
Cost function
Model accuracy
Confusion matrix
Predictor variables
Response variable
Dimensionality reduction
Class imbalance problem
Model bias and variance
Underfitting and overfitting
Data preprocessing
Holdout sample
Hyperparameter tuning
Performance metrics
Feature engineering
Model interpretability
ML project pipeline
Business understanding
Understanding and sourcing the data
Preparing the data
Model building and evaluation
Model deployment
Learning paradigm
Datasets
Summary
Chapter 2: Predicting Employee Attrition Using Ensemble Models
Philosophy behind ensembling
Getting started
Understanding the attrition problem and the dataset
K-nearest neighbors model for benchmarking the performance
Bagging
Bagged classification and regression trees (treeBag) implementation
Support vector machine bagging (SVMBag) implementation
Naive Bayes (nbBag) bagging implementation
Randomization with random forests
Implementing an attrition prediction model with random forests
Boosting
The GBM implementation
Building attrition prediction model with XGBoost
Stacking
Building attrition prediction model with stacking
Summary
Chapter 3: Implementing a Jokes Recommendation Engine
Fundamental aspects of recommendation engines
Recommendation engine categories
Content-based filtering
Collaborative filtering
Hybrid filtering
Getting started
Understanding the Jokes recommendation problem and the dataset
Converting the DataFrame
Dividing the DataFrame
Building a recommendation system with an item-based collaborative filtering technique
Building a recommendation system with a user-based collaborative filtering technique
Building a recommendation system based on an association-rule mining technique
The Apriori algorithm
Content-based recommendation engine
Differentiating between ITCF and content-based recommendations
Building a hybrid recommendation system for Jokes recommendations
Summary
References
Chapter 4: Sentiment Analysis of Amazon Reviews with NLP
The sentiment analysis problem
Getting started
Understanding the Amazon reviews dataset
Building a text sentiment classifier with the BoW approach
Pros and cons of the BoW approach
Understanding word embedding
Building a text sentiment classifier with pretrained word2vec word embedding based on Reuters news corpus
Building a text sentiment classifier with GloVe word embedding
Building a text sentiment classifier with fastText
Summary
Chapter 5: Customer Segmentation Using Wholesale Data
Understanding customer segmentation
Understanding the wholesale customer dataset and the segmentation problem
Categories of clustering algorithms
Identifying the customer segments in wholesale customer data using k-means clustering
Working mechanics of the k-means algorithm
Identifying the customer segments in the wholesale customer data using DIANA
Identifying the customer segments in the wholesale customers data using AGNES
Summary
Chapter 6: Image Recognition Using Deep Neural Networks
Technical requirements
Understanding computer vision
Achieving computer vision with deep learning
Convolutional Neural Networks
Layers of CNNs
Introduction to the MXNet framework
Understanding the MNIST dataset
Implementing a deep learning network for handwritten digit recognition
Implementing dropout to avoid overfitting
Implementing the LeNet architecture with the MXNet library
Implementing computer vision with pretrained models
Summary
Chapter 7: Credit Card Fraud Detection Using Autoencoders
Machine learning in credit card fraud detection
Autoencoders explained
Types of AEs based on hidden layers
Types of AEs based on restrictions
Applications of AEs
The credit card fraud dataset
Building AEs with the H2O library in R
Autoencoder code implementation for credit card fraud detection
Summary
Chapter 8: Automatic Prose Generation with Recurrent Neural Networks
Understanding language models
Exploring recurrent neural networks
Comparison of feedforward neural networks and RNNs
Backpropagation through time
Problems and solutions to gradients in RNN
Exploding gradients
Vanishing gradients
Building an automated prose generator with an RNN
Implementing the project
Summary
Chapter 9: Winning the Casino Slot Machines with Reinforcement Learning
Understanding RL
Comparison of RL with other ML algorithms
Terminology of RL
The multi-arm bandit problem
Strategies for solving MABP
The epsilon-greedy algorithm
Boltzmann or softmax exploration
Decayed epsilon greedy
The upper confidence bound algorithm
Thompson sampling
Multi-arm bandit - real-world use cases
Solving the MABP with UCB and Thompson sampling algorithms
Summary
Appendix: The Road Ahead
Other Books You May Enjoy
Index

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