Difficulty Level
Intermediate
Completion Time
11h4m
Language
English
About Book
Who Is This Book For?
If you are a statistician, chief information officer, data scientist, ML engineer, ML practitioner, quantitative analyst, and student of machine learning, this is the book for you. You should have basic knowledge of the use of R. Readers without previous experience of programming in R will also be able to use the tools in the book.
Learning Predictive Analytics with R
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 11h4m total length
Setting GNU R for predictive modeling
Basic data visualization with tools built-in in R
Data visualization with lattice
Unsupervized learning: clustering with k-means
Unsupervized learning: Hierarchical clustering
Unsupervized learning: Principal Component Analysis
Unsupervized learning: market basket analyses with Apriori (association rules)
Probability Distributions, Covariance, and Correlation
Regression
Classification with naïve Bayes and k-nn
Decision trees
Multilevel regression in R
Text Analytics with R
PMML
Appendix, Solution to exercises
References
Related Resources
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