R: Unleash Machine Learning Techniques

Name: R: Unleash Machine Learning Techniques | Smarter data analytics
Brand: Packt Publishing
Availability: OnlineOnly

Smarter data analytics

Dipanjan Sarkar Brett Lantz Raghav Bali Cory Lesmeister(Author)

Packt Publishing

Published on 15. April 2025

1123 pages

E-Book

ePUB with Adobe-DRM

System requirements

E-Book

PDF with Adobe-DRM

System requirements

978-1-78712-828-6 (ISBN)

from €70.99

Available for download

Watchlist: see prices

Description

All prices

More details

Other editions

Persons

Sarkar Dipanjan :
Dipanjan (DJ) Sarkar is a Data Scientist at Intel, leveraging data science, machine learning, and deep learning to build large-scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. He has been an analytics practitioner for several years now, specializing in machine learning, NLP, statistical methods, and deep learning. He is passionate about education and also acts as a Data Science Mentor at various organizations like Springboard, helping people learn data science. He is also a key contributor and editor for Towards Data Science, a leading online journal on AI and Data Science. He has also authored several books on R, Python, machine learning, NLP, and deep learning.Lantz Brett :

Brett Lantz (DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at Data Spelunking, a website dedicated to sharing knowledge about the search for insight in data.Bali Raghav :

Raghav Bali is a Staff Data Scientist at Delivery Hero, a leading food delivery service headquartered in Berlin, Germany. With 12+ years of expertise, he specializes in research and development of enterprise-level solutions leveraging Machine Learning, Deep Learning, Natural Language Processing, and Recommendation Engines for practical business applications. Besides his professional endeavors, Raghav is an esteemed mentor and an accomplished public speaker. He has contributed to multiple peer-reviewed papers and authored multiple well received books. Additionally, he holds co-inventor credits on multiple patents in healthcare, machine learning, deep learning, and natural language processing.Lesmeister Cory :

Cory Lesmeister has over fourteen years of quantitative experience and is currently a senior data scientist for the advanced analytics team at Cummins, Inc. in Columbus, Indiana. He has 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. Here, he served 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. He has a BBA in aviation administration from the University of North Dakota and a commercial helicopter license.

Content

Intro
Copyright
Credits
Preface
Table of Content
Module 1: R Machine Learning By Example
Chapter 1: Getting Started with R and Machine Learning
Delving into the basics of R
Data structures in R
Working with functions
Controlling code flow
Advanced constructs
Next steps with R
Machine learning basics
Summary
Chapter 2: Let's Help Machines Learn
Understanding machine learning
Algorithms in machine learning
Families of algorithms
Summary
Chapter 3: Predicting Customer Shopping Trends with Market Basket Analysis
Detecting and predicting trends
Market basket analysis
Evaluating a product contingency matrix
Frequent itemset generation
Association rule mining
Summary
Chapter 4: Building a Product Recommendation System
Understanding recommendation systems
Issues with recommendation systems
Collaborative filters
Building a recommender engine
Production ready recommender engines
Summary
Chapter 5: Credit Risk Detection and Prediction - Descriptive Analytics
Types of analytics
Our next challenge
What is credit risk?
Getting the data
Data preprocessing
Data analysis and transformation
Next steps
Summary
Chapter 6: Credit Risk Detection and Prediction - Predictive Analytics
Predictive analytics
How to predict credit risk
Important concepts in predictive modeling
Getting the data
Data preprocessing
Feature selection
Modeling using logistic regression
Modeling using support vector machines
Modeling using decision trees
Modeling using random forests
Modeling using neural networks
Model comparison and selection
Summary
Chapter 7: Social Media Analysis - Analyzing Twitter Data
Social networks (Twitter)
Data mining @social networks
Getting started with Twitter APIs
Twitter data mining
Challenges with social network data mining
References
Summary
Chapter 8: Sentiment Analysis of Twitter Data
Understanding Sentiment Analysis
Sentiment analysis upon Tweets
Summary
Module 2: Machine Learning with R
Chapter 1: Introducing Machine Learning
The origins of machine learning
Uses and abuses of machine learning
How machines learn
Machine learning in practice
Machine learning with R
Summary
Chapter 2: Managing and Understanding Data
R data structures
Managing data with R
Exploring and understanding data
Summary
Chapter 3: Lazy Learning - Classification Using Nearest Neighbors
Understanding nearest neighbor classification
Example - diagnosing breast cancer with the k-NN algorithm
Summary
Chapter 4: Probabilistic Learning - Classification Using Naive Bayes
Understanding Naive Bayes
Example - filtering mobile phone spam with the Naive Bayes algorithm
Summary
Chapter 5: Divide and Conquer - Classification Using Decision Trees and Rules
Understanding decision trees
Example - identifying risky bank loans using C5.0 decision trees
Understanding classification rules
Example - identifying poisonous mushrooms with rule learners
Summary
Chapter 6: Forecasting Numeric Data - Regression Methods
Understanding regression
Example - predicting medical expenses using linear regression
Understanding regression trees and model trees
Example - estimating the quality of wines with regression trees and model trees
Summary
Chapter 7: Black Box Methods - Neural Networks and Support Vector Machines
Understanding neural networks
Example - Modeling the strength of concrete with ANNs
Understanding Support Vector Machines
Example - performing OCR with SVMs
Chapter 8: Finding Patterns - Market Basket Analysis Using Association Rules
Understanding association rules
Example - identifying frequently purchased groceries with association rules
Summary
Chapter 9: Finding Groups of Data - Clustering with k-means
Understanding clustering
Example - finding teen market segments using k-means clustering
Summary
Chapter 10: Evaluating Model Performance
Measuring performance for classification
Estimating future performance
Summary
Chapter 11: Improving Model Performance
Tuning stock models for better performance
Improving model performance with meta-learning
Summary
Chapter 12: Specialized Machine Learning Topics
Working with proprietary files and databases
Working with online data and services
Working with domain-specific data
Improving the performance of R
Summary
Module 3: Mastering Machine Learning with R
Chapter 1: A Process for Success
The process
Business understanding
Data understanding
Data preparation
Modeling
Evaluation
Deployment
Algorithm flowchart
Summary
Chapter 2: Linear Regression - The Blocking and Tackling of Machine Learning
Univariate linear regression
Multivariate linear regression
Other linear model considerations
Summary
Chapter 3: Logistic Regression and Discriminant Analysis
Classification methods and linear regression
Logistic regression
Model selection
Summary
Chapter 4: Advanced Feature Selection in Linear Models
Regularization in a nutshell
Business case
Modeling and evaluation
Model selection
Summary
Chapter 5: More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
K-Nearest Neighbors
Support Vector Machines
Business case
Feature selection for SVMs
Summary
Chapter 6: Classification and Regression Trees
Introduction
An overview of the techniques
Business case
Summary
Chapter 7: Neural Networks
Neural network
Deep learning, a not-so-deep overview
Business understanding
Data understanding and preparation
Modeling and evaluation
An example of deep learning
Summary
Chapter 8: Cluster Analysis
Hierarchical clustering
K-means clustering
Gower and partitioning around medoids
Data understanding and preparation
Modeling and evaluation
Summary
Chapter 9: Principal Components Analysis
An overview of the principal components
Modeling and evaluation
Summary
Chapter 10: Market Basket Analysis and Recommendation Engines
An overview of a market basket analysis
Business understanding
Data understanding and preparation
Modeling and evaluation
An overview of a recommendation engine
Business understanding and recommendations
Data understanding, preparation, and recommendations
Modeling, evaluation, and recommendations
Summary
Chapter 11: Time Series and Causality
Univariate time series analysis
Modeling and evaluation
Summary
Chapter 12: Text Mining
Text mining framework and methods
Topic models
Modeling and evaluation
Summary
Appendix: R Fundamentals
Introduction
Getting R up and running
Using R
Data frames and matrices
Summary stats
Installing and loading the R packages
Summary
Bibliography

System requirements

File format: ePUB
Copy protection: Adobe-DRM (Digital Rights Management)

System requirements:

Computer (Windows; MacOS X; Linux): Install the free reader Adobe Digital Editions prior to download (see eBook Help).
Tablet/smartphone (Android; iOS): Install the free app Adobe Digital Editions or the app PocketBook before downloading (see eBook Help).
E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (not Kindle).

The file format ePub works well for novels and non-fiction books – i.e., „flowing” text without complex layout. On an e-reader or smartphone, line and page breaks automatically adjust to fit the small displays.
This eBook uses Adobe-DRM, a „hard” copy protection. If the necessary requirements are not met, unfortunately you will not be able to open the eBook. You will therefore need to prepare your reading hardware before downloading.

Please note: We strongly recommend that you authorise using your personal Adobe ID after installation of any reading software.

For more information, see our ebook Help page.

File format: PDF
Copy-Protection: Adobe-DRM (Digital Rights Management)

System requirements:

Computer (Windows; MacOS X; Linux): Install the free reader Adobe Digital Editions prior to download (see eBook Help).
Tablet/smartphone (Android; iOS): Install the free app Adobe Digital Editions or the app PocketBook before downloading (see eBook Help).
E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (only limited: Kindle).

The file format PDF always displays a book page identically on any hardware. This makes PDF suitable for complex layouts such as those used in textbooks and reference books (images, tables, columns, footnotes). Unfortunately, on the small screens of e-readers or smartphones, PDFs are rather annoying, requiring too much scrolling.
This eBook uses Adobe-DRM, a „hard” copy protection. If the necessary requirements are not met, unfortunately you will not be able to open the eBook. You will therefore need to prepare your reading hardware before downloading.

Please note: We strongly recommend that you authorise using your personal Adobe ID after installation of any reading software.

For more information, see our eBook Help page.

Save as PDF Copy link into clipboard

Schweitzer Fachinformationen

R: Unleash Machine Learning Techniques

Description

All prices

More details

Other editions

Additional editions

Persons

Content

System requirements