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R Machine Learning Projects电子书

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1人正在读 | 0人评论 9.8

作       者:Dr. Sunil Kumar Chinnamgari

出  版  社:Packt Publishing

出版时间:2019-01-14

字       数:43.2万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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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.
目录展开

Title Page

Copyright and Credits

R Machine Learning Projects

About Packt

Why subscribe?

Packt.com

Dedication

Contributors

About the author

About the reviewers

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

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

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

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

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

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

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

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

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

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

The Road Ahead

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