Advanced Machine Learning with R

Tackle data analytics and machine learning challenges and build complex applications with R 3.5

Cory Lesmeister , Dr. Sunil Kumar Chinnamgari 지음

Packt(GCO Science) 2019년 05월 20일 출간

작품소개

책소개

▶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

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

▶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.

목차

▶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 Landscape
15. Predicting Employee Attrition Using Ensemble Models
16. Implementing a Jokes 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

책 속으로

▶What this book covers
- Chapter 1, Preparing and Understanding Data, covers the loading of data and demonstrates how to obtain an understanding of its structure and dimensions, as well as how to install the necessary packages.

- Chapter 2, Linear Regression, provides you with a solid foundation before learning advanced methods such as Support Vector Machines and Gradient Boosting. No more solid foundation exists than the least squares linear regression.

- Chapter 3, Logistic Regression, presents a discussion on how logistic regression and discriminant analysis is used in order to predict a categorical outcome. Multivariate adaptive regression splines have been added. This technique performs well, handles nonlinearity, and is easy to explain.

- Chapter 4, Advanced Feature Selection in Linear Models, shows regularization techniques to help improve the predictive ability and interpretability as feature selection is a critical and often extremely challenging component of machine learning. It also includes techniques not only for regression but also for a classification problem.

- Chapter 5, K-Nearest Neighbors and Support Vector Machines, begins the exploration of the more advanced and nonlinear techniques. The real power of machine learning will be unveiled.

- Chapter 6, Tree-Based Classification, offers some of the most powerful predictive abilities of all the machine learning techniques, especially for classification problems. Single decision trees will be discussed along with the more advanced random forests and boosted trees. It also contains very popular techniques provided by the XGBOOST package.

- Chapter 7, Neural Networks and Deep Learning, shows some of the most exciting machine learning methods currently used. Inspired by how the brain works, neural networks and their more recent and advanced offshoot, Deep Learning, will be put to the test. It also includes code for the H2O package, including hyperparameter search.

- Chapter 8, Creating Ensembles and Multiclass Methods, has completely new content, involving the utilization of several great packages.

- Chapter 9, Cluster Analysis, covers unsupervised learning. Instead of trying to make a prediction, the goal will focus on uncovering the latent structure of observations. Three clustering methods will be discussed: hierarchical, k-means, and partitioning around medoids. It also includes the methodology for executing unsupervised learning with random forests.

- Chapter 10, Principal Component Analysis, continues the examination of unsupervised learning with principal components analysis, which is used to uncover the latent structure of the features. Once this is done, the new features will be used in a supervised learning exercise.

- Chapter 11, Association Analysis, explains association analysis and applies not only to making recommendations, product placement, and promotional pricing, but can also be used in manufacturing, web usage, and healthcare.

- Chapter 12, Time Series and Causality, discusses univariate forecast models, bivariate regression, and Granger causality models, including an analysis of carbon emissions and climate change, along with a demonstration of different causality test methods.

- Chapter 13, Text Mining, demonstrates a framework for quantitative text mining and the building of topic models. Along with time series, the world of data contains vast volumes of data in a textual format. With so much data as text, it is critically important to understand how to manipulate, code, and analyze the data in order to provide meaningful insights.

- Chapter 14, Exploring the Machine Learning Landscape, will briefly review the various ML concepts that a practitioner must know. In this chapter, we will cover topics such as supervised learning, reinforcement learning, unsupervised learning, and real-world ML uses cases.

- Chapter 15, Predicting Employee Attrition Using Ensemble Models,...

출판사 서평

▶ Preface
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

작가정보

저자(글) Cory Lesmeister

Cory Lesmeister has over 14 years of quantitative experience and is currently a senior data scientist for the advanced analytics team at Cummins, Inc. in Columbus, Indiana. Cory spent 16 years at Eli Lilly and Company in sales, market research, Lean Six Sigma, marketing analytics, and new product forecasting. He also has several years of experience in the insurance and banking industries, both as a consultant and as a manager of marketing analytics. A former US Army active duty and reserve officer, Cory was stationed in Baghdad, Iraq, in 2009 serving as the strategic advisor to the 29,000-person Iraqi Oil Police, succeeding where others failed by acquiring and delivering promised equipment to help the country secure and protect its oil infrastructure. Cory has a BBA in Aviation Administration from the University of North Dakota and a commercial helicopter license.

저자(글) Dr. Sunil Kumar Chinnamgari

Dr. Sunil Kumar Chinnamgari has a PhD in computer science (specializing in machine learning and natural language processing). He is an AI researcher with more than 14 years of industry experience. Currently, he works in the capacity of a lead data scientist with a US financial giant. He has published several research papers in Scopus and IEEE journals, and is a frequent speaker at various meet-ups. He is an avid coder and has won multiple hackathons. In his spare time, Sunil likes to teach, travel, and spend time with family.