Additional information
by Viswa Viswanathan (Author), Shanthi Viswanathan (Author), Atmajitsinh Gohil (Author)
Get savvy with R language and actualize projects aimed at analysis, visualization and machine learning
Key Features:
- Proficiently analyze data and apply machine learning techniques
- Generate visualizations, develop interactive visualizations and applications to understand various data exploratory functions in R
- Construct a predictive model by using a variety of machine learning packages
Book Description:
The 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 Learning Path is chock-full of recipes. Literally! It aims to excite you with awesome projects focused on analysis, visualization, and machine learning. We'll start off with data analysis - this will show you ways to use R to generate professional analysis reports. We'll then move on to visualizing our data - this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we'll move into the world of machine learning - this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction.
This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:
R Data Analysis Cookbook by Viswa Viswanathan and Shanthi Viswanathan
R Data Visualization Cookbook by Atmajitsinh Gohil
Machine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu)
What You Will Learn:
- Get data into your R environment and prepare it for analysis
- Perform exploratory data analyses and generate meaningful visualizations of the data
- Generate various plots in R using the basic R plotting techniques
- Create presentations and learn the basics of creating apps in R for your audience
- Create and inspect the transaction dataset, performing association analysis with the Apriori algorithm
- Visualize associations in various graph formats and find frequent itemset using the ECLAT algorithm
- Build, tune, and evaluate predictive models with different machine learning packages
- Incorporate R and Hadoop to solve machine learning problems on big data-
Who this book is for:
This Learning Path is ideal for those who have been exposed to R, but have not used it extensively yet. It covers the basics of using R and is written for new and intermediate R users interested in learning. This Learning Path also provides in-depth insights into professional techniques for analysis, visualization, and machine learning with R - it will help you increase your R expertise, regardless of your level of experience.
