Book description
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.
Table of contents
- Title Page
- Copyright and Credits
- About Packt
- Dedication
- Contributors
- Preface
-
Exploring the Machine Learning Landscape
- ML versus software engineering
- Types of ML methods
-
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
- Learning paradigm
- Datasets
- Summary
- Predicting Employee Attrition Using Ensemble Models
-
Implementing a Jokes Recommendation Engine
- Fundamental aspects of recommendation engines
- Getting started
- Understanding the Jokes recommendation problem and the dataset
- 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
- Content-based recommendation engine
- 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
- 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
- Identifying the customer segments in wholesale customer data using k-means clustering
- 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
- Credit Card Fraud Detection Using Autoencoders
- Automatic Prose Generation with Recurrent Neural Networks
- Winning the Casino Slot Machines with Reinforcement Learning
- The Road Ahead
- Other Books You May Enjoy
Product information
- Title: R Machine Learning Projects
- Author(s):
- Release date: January 2019
- Publisher(s): Packt Publishing
- ISBN: 9781789807943
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