Book description
Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner.
About This Book
Build your confidence with R and find out how to solve a huge range of data-related problems
Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today
Don’t just learn – apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysis
Who This Book Is For
Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science
What You Will Learn
Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action
Solve interesting real-world problems using machine learning and R as the journey unfolds
Write reusable code and build complete machine learning systems from the ground up
Learn specialized machine learning techniques for text mining, social network data, big data, and more
Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
Evaluate and improve the performance of machine learning models
Learn specialized machine learning techniques for text mining, social network data, big data, and more
In Detail
R is the established language of data analysts and statisticians around the world. And you shouldn’t be afraid to use it…
This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R.
In the first module you’ll get to grips with the fundamentals of R. This means you’ll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems.
For the following two modules we’ll begin to investigate machine learning algorithms in more detail. To build upon the basics, you’ll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they’re all focused on solving real problems in different areas, ranging from finance to social media.
This Learning Path has been curated from three Packt products:
R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar
Machine Learning with R Learning - Second Edition By Brett Lantz
Mastering Machine Learning with R By Cory Lesmeister
Style and approach
This is an enticing learning path that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.
Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.
Table of contents
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R: Unleash Machine Learning Techniques
- Table of Contents
- R: Unleash Machine Learning Techniques
- R: Unleash Machine Learning Techniques
- Credits
- Preface
-
I. Module 1
- 1. Getting Started with R and Machine Learning
- 2. Let's Help Machines Learn
- 3. Predicting Customer Shopping Trends with Market Basket Analysis
- 4. Building a Product Recommendation System
- 5. Credit Risk Detection and Prediction – Descriptive Analytics
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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
- 7. Social Media Analysis – Analyzing Twitter Data
- 8. Sentiment Analysis of Twitter Data
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II. Module 2
- 1. Introducing Machine Learning
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2. Managing and Understanding Data
- R data structures
- Managing data with R
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Exploring and understanding data
- Exploring the structure of data
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Exploring numeric variables
- Measuring the central tendency – mean and median
- Measuring spread – quartiles and the five-number summary
- Visualizing numeric variables – boxplots
- Visualizing numeric variables – histograms
- Understanding numeric data – uniform and normal distributions
- Measuring spread – variance and standard deviation
- Exploring categorical variables
- Exploring relationships between variables
- Summary
- 3. Lazy Learning – Classification Using Nearest Neighbors
- 4. Probabilistic Learning – Classification Using Naive Bayes
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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
-
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
- 7. Black Box Methods – Neural Networks and Support Vector Machines
- 8. Finding Patterns – Market Basket Analysis Using Association Rules
- 9. Finding Groups of Data – Clustering with k-means
- 10. Evaluating Model Performance
- 11. Improving Model Performance
- 12. Specialized Machine Learning Topics
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III. Module 3
- 1. A Process for Success
- 2. Linear Regression – The Blocking and Tackling of Machine Learning
- 3. Logistic Regression and Discriminant Analysis
- 4. Advanced Feature Selection in Linear Models
- 5. More Classification Techniques – K-Nearest Neighbors and Support Vector Machines
- 6. Classification and Regression Trees
- 7. Neural Networks
- 8. Cluster Analysis
- 9. Principal Components Analysis
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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
- 11. Time Series and Causality
- 12. Text Mining
- A. R Fundamentals
- A. Bibliography
- Index
Product information
- Title: R: Unleash Machine Learning Techniques
- Author(s):
- Release date: October 2016
- Publisher(s): Packt Publishing
- ISBN: 9781787127340
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