Mastering Machine Learning with R
| Author | : | |
| Rating | : | 4.47 (802 Votes) |
| Asin | : | B013GKGL8U |
| Format Type | : | |
| Number of Pages | : | 163 Pages |
| Publish Date | : | 2016-01-06 |
| Language | : | English |
DESCRIPTION:
Army Reservist, Cory was in Baghdad, Iraq, in 2009 as a strategic advisor to the 29,000-person Iraqi oil police, where he supplied equipment to help the country secure and protect its oil infrastructure. An aviation aficionado, Cory has a BBA in aviation administration from the University of North Dakota and a co
Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning to your data.The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful.What You Will LearnGain deep insights to learn the applications of machine learning tools to the industryManipulate data in R efficiently to prepare it for analysisMaster the skill of recognizing techniques for effective visualization of dataUnderstand why and how to create test and training data sets for analysisFamiliarize yourself with fundamental learning methods such as linear and logistic regressionComprehend advanced learning methods such as support vector machinesRealize why and how to apply unsupervised learning methodsIn DetailMachine learning is a field of Artificial Intelligence to build systems that learn f
Army Reservist, Cory was in Baghdad, Iraq, in 2009 as a strategic advisor to the 29,000-person Iraqi oil police, where he supplied equipment to help the country secure and protect its oil infrastructure. Cory lives in Carmel, IN, with his wife and their two teenage daughters.. About the AuthorCory LesmeisterCory Lesmeister currently works as an advanced analytics consultant for Clarity Solution Group, where he applies the methods in this book to solve complex problems and provide actionable insights. An avia
Great Introduction to ML with R HDFS_Python Overall, I think the book was good and I enjoyed reading it, for a statistics book this is a praise. The following pros will seem lacking to the cons but believe me that it is because the book was overall good and any compliment hits nearly all chapters in the book. When I did see a con, I expanded on it to give full insight into the issue. As in any endeavor of this sort, it is always a challenge to find the right balance between theory and application.Pros:The book contains companion code. This means a student can save the code for the future, load it in when necessary, and alter the. Poor Dimitri Shvorob Even a dog can publish a book with Packt: the publisher operates a no-editors, no-graphic-designers, no-standards business model and publishes anything by anybody, hoping that something sticks. In the hot "data science / machine learning" segment, Packt met 2015 with a bang, churning out the god-awful "Learning Data Mining with R" by Makhabel and "R for Data Science" by Toomey. Then four more titles came out:"Mastering Predictive Analytics with R" by Forte, $45"Mastering Machine Learning with R" by Lesmeister, $40"R Data Analysis Cookbook" by Viswanathan and Viswanathan, $40"Machine Le