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Mastering R Programming

Mastering R Programming


This video covers advanced-level concepts in R programming and demonstrates industry best practices. This is an advanced R course with an intensive focus on machine learning concepts in depth and applying them in the real world with R.

Overview

Description
Build R packages, gain in-depth knowledge of machine learning, and master advanced programming techniques in R.

R is a statistical programming language that allows you to build probabilistic models, perform data science, and build machine learning algorithms. R has a great package ecosystem that enables developers to conduct data visualization to data analysis.This video covers advanced-level concepts in R programming and demonstrates industry best practices. This is an advanced R course with an intensive focus on machine learning concepts in depth and applying them in the real world with R.

We start off with pre-model-building activities such as univariate and bivariate analysis, outlier detection, and missing value treatment featuring the mice package. We then take a look linear and non-linear regression modeling and classification models, and check out the math behind the working of classification algorithms. We then shift our focus to unsupervised learning algorithms, time series analysis and forecasting models, and text analytics. We will see how to create a Term Document Matrix, normalize with TF-IDF, and draw a word cloud. We’ll also check out how cosine similarity can be used to score similar documents and how Latent Semantic Indexing (LSI) can be used as a vector space model to group similar documents. Later, the course delves into constructing charts using the Ggplot2 package and multiple strategies to speed up R code. We then go over the powerful `dplyr` and `data.table` packages and familiarize ourselves to work with the pipe operator during the process. We will learn to write and interface C++ code in R using the powerful Rcpp package. We’ll complete our journey with building an R package using facilities from the roxygen2 and dev tools packages.

By the end of the course, you will have a solid knowledge of machine learning and the R language itself. You’ll also solve numerous coding challenges throughout the course.

About the Author

Selva Prabhakaran is a data scientist with a large E-commerce organization. In his 7 years of experience in data science, he has tackled complex real-world data science problems and delivered production-grade solutions for top multinational companies. Selva lives in Bangalore with his wife. He can follow him on Twitter athttp://www.twitter.com/r_programming and he periodically writes at http://r-statistics.co.
Basic knowledge
Basic knowledge of R would be helpful
It assumes you are somewhat familiar working with the R language

Course Information

Basic knowledge of R would be helpful
It assumes you are somewhat familiar working with the R language

Perform pre-model-building steps
Get an in-depth view of linear and non-linear regression modeling
Build and evaluate classification models
Master the use of the powerful caret package
Understand the working behind core machine learning algorithms
Implement unsupervised learning algorithms
Build recommendation engines using multiple algorithms
Analyze time series data and build forecasting models
Delve in depth into text analytics
Interface C++ code in R using Rcpp
Construct nice looking charts with Ggplot2
Get to know advanced strategies to speed up R code
Build R packages from scratch and submit them to CRAN

The video is for machine learning engineers, statisticians, and data scientists.

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Course Specifications

IT and Computing courses are available to study on our learning platform. 

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Adult education is the non-credential activity of gaining skills and improved education. 

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Online education is electronically supported learning that relies on the Internet for teacher/student interaction. 

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A short course is a learning programme that gives you combined content or specific skills training in a short period of time. Short courses often lean towards the more practical side of things and have less theory than a university course – this gives you a more hands-on experience within your field of interest.

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Course duration is 24 hours.

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