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Machine Learning With R Cookbook - 110 Recipes For Building Powerful Predictive Models With R

Key FeaturesApply R to simplify predictive modeling with short and simple codeUse machine learning to solve problems ranging from small to big dataBuild a training and testing dataset from the churn dataset, applying different classification methodsBook DescriptionThe R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics.This book covers the basics of R by setting up a user-friendly programming environment and performing data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationships. You will then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimension reduction.What you will learnCreate and inspect the transaction dataset, performing association analysis with the Apriori algorithmVisualize patterns and associations using a range of graphs and find frequent itemsets using the Eclat algorithmCompare differences between each regression method to discover how they solve problemsPredict possible churn users with the classification approachImplement the clustering method to segment customer dataCompress images with the dimension reduction methodIncorporate R and Hadoop to solve machine learning problems on Big DataAbout the AuthorYu-Wei, Chiu (David Chiu) is the founder of LargitData. He has previously worked for Trend Micro as a software engineer, with the responsibility of building big data platforms for business intelligence and customer relationship management systems. In addition to being a start-up entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis.Table of ContentsPractical Machine Learning with RData Exploration with RMS TitanicR and StatisticsUnderstanding Regression AnalysisClassification (I) – Tree, Lazy, and ProbabilisticClassification (II) – Neural Network and SVMModel EvaluationEnsemble LearningClusteringAssociation Analysis and Sequence MininDimension ReductionBig Data Analysis (R and Hadoop)

File Size: 19508 KB

Print Length: 442 pages

Publisher: Packt Publishing (March 26, 2015)

Publication Date: March 26, 2015

Sold by:  Digital Services LLC

Language: English


Text-to-Speech: Enabled

X-Ray: Not Enabled

Word Wise: Not Enabled

Lending: Not Enabled

Enhanced Typesetting: Enabled

Best Sellers Rank: #668,110 Paid in Kindle Store (See Top 100 Paid in Kindle Store) #416 in Kindle Store > Kindle eBooks > Computers & Technology > Web Graphics #517 in Books > Computers & Technology > Databases & Big Data > Data Modeling & Design #717 in Books > Computers & Technology > Software > Mathematical & Statistical

I got the free book "Machine Learning with R Cookbook"from Packt publishers for a review. I really thank the publisher for givingme the opportunity to have a good read on this book and I really appreciatethe work done by the author "Yu-Wei Chiu". The following is the briefsnapshot of my review.To say the foreword about the book, Machine Learning and Statistics are madesimple with R leading this to the competitive level of Python and otheropen source communities.Chapters -1 & 2------------------------------------------------------------------The author provided each detail about R installation and resources veryclearly. This avoids beginners to google the basics.Cons : 1. This assumes some basic programming in R especiallyData management/handling.Chapters -3 to 7------------------------------------------------------------------All R statistical tools are provided with clear examples in 3rd Chapter.This gives very quick view of R-Statistical strength. The later chaptersare sufficient enough to explain various types of Regression,Classificationmethods. This shows a valid comparison between Python's Scikit-Learnand R.Cons : 1. This expects basic knowledge of Statistics and R utilities foravailable statistical methods are explored.2. This does not explain for what sort of data/application what methodsare feasible. Reader should explore this by his/her own knowledge.Chapters -8 to 9------------------------------------------------------------------Chapter -8 explains the Boosting/Bagging approaches.

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