Mastering R Programming
Mastering R Programming. Build R packages, gain in-depth knowledge of machine learning, and master advanced programming techniques in R
The name of this course is Mastering R Programming. The knowledge you will get with this indescribable online course is astonishing. Build R packages, gain in-depth knowledge of machine learning, and master advanced programming techniques in R.
Not only will you be able to deeply internalize the concepts, but also their application in different fields won’t ever be a problem. The instructor is Packt Publishing, one of the very best experts in this field.
Description of this course: Mastering R Programming
Course Description 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 vivo 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 AuthorSelva 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.
Requirements of this course: Mastering R Programming
What are the requirements? Basic knowledge of R would be helpful.It assumes you are somewhat íntimo working with the R language. This is a task-based video course with hands-on working sessions and detailed explanations. Most videos in this course close with a related coding challenge.You will see hands-on coding sessions throughout and get in-depthexplanations ofthe concepts
What will you learn in this course: Mastering R Programming?
What am I going to get from this course? 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
Target audience of this course: Mastering R Programming
Who is the target audience? The video is for machine learning engineers, statisticians, and data scientists.