Mastering R Programming
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VIDEO TUTORIAL Mastering R Programming

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

About This R Video tutorial

  • This video course showcases the power and depth of R programming when it comes to high performance and data analysis
  • It covers concepts of data analysis, machine learning, and statistical modeling
  • Develop R packages and extend the functionality of your model

Mastering R Programming In Detail

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 R video course 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.

What will you learn in this course?

Course plan
Chapter 1
Pre-Model Building Steps
Chapter 2
Regression Modelling - In Depth
Chapter 3
Classification Models and caret Package - In Depth
Chapter 4
Core Machine Learning - In Depth
Chapter 5
Unsupervised Learning
Chapter 6
Time Series Analysis and Forecasting
Chapter 7
Text Analytics - In Depth

Detailed course plan

Chapter 1 : Pre-Model Building Steps
Lesson 1The Course Overview
Lesson 2Performing Univariate Analysis
Lesson 3Bivariate Analysis – Correlation, Chi-Sq Test, and ANOVA
Lesson 4Detecting and Treating Outlier
Lesson 5Treating Missing Values with `mice`
Chapter 2 : Regression Modelling - In Depth
Lesson 1Building Linear Regressors
Lesson 2Interpreting Regression Results and Interactions Terms
Lesson 3Performing Residual Analysis and Extracting Extreme Observations With Cook's Distance
Lesson 4Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA
Lesson 5Validating Model Performance on New Data with k-Fold Cross Validation
Lesson 6Building Non-Linear Regressors with Splines and GAMs
Chapter 3 : Classification Models and caret Package - In Depth
Lesson 1Building Logistic Regressors, Evaluation Metrics, and ROC Curve
Lesson 2Understanding the Concept and Building Naive Bayes Classifier
Lesson 3Building k-Nearest Neighbors Classifier
Lesson 4Building Tree Based Models Using RPart, cTree, and C5.0
Lesson 5Building Predictive Models with the caret Package
Lesson 6Selecting Important Features with RFE, varImp, and Boruta
Chapter 4 : Core Machine Learning - In Depth
Lesson 1Building Classifiers with Support Vector Machines
Lesson 2Understanding Bagging and Building Random Forest Classifier
Lesson 3Implementing Stochastic Gradient Boosting with GBM
Lesson 4Regularization with Ridge, Lasso, and Elasticnet
Lesson 5Building Classifiers and Regressors with XGBoost
Chapter 5 : Unsupervised Learning
Lesson 1Dimensionality Reduction with Principal Component Analysis
Lesson 2Clustering with k-means and Principal Components
Lesson 3Determining Optimum Number of Clusters
Lesson 4Understanding and Implementing Hierarchical Clustering
Lesson 5Clustering with Affinity Propagation
Lesson 6Building Recommendation Engines
Chapter 6 : Time Series Analysis and Forecasting
Lesson 1Understanding the Components of a Time Series, and the xts Package
Lesson 2Stationarity, De-Trend, and De-Seasonalize
Lesson 3Understanding the Significance of Lags, ACF, PACF, and CCF
Lesson 4Forecasting with Moving Average and Exponential Smoothing
Lesson 5Forecasting with Double Exponential and Holt Winters
Lesson 6Forecasting with ARIMA Modelling
Chapter 7 : Text Analytics - In Depth
Lesson 1Scraping Web Pages and Processing Texts
Lesson 2Corpus, TDM, TF-IDF, and Word Cloud
Lesson 3Cosine Similarity and Latent Semantic Analysis
Lesson 4Extracting Topics with Latent Dirichlet Allocation
Lesson 5Sentiment Scoring with tidytext and Syuzhet
Lesson 6Classifying Texts with RTextTools
Chapter 8 : Ggplot2 - Core Knowledge
Lesson 1Building a Basic ggplot2 and Customizing the Aesthetics and Themes
Lesson 2Manipulating Legend, AddingText, and Annotation
Lesson 3Drawing Multiple Plots with Faceting and Changing Layouts
Lesson 4Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots
Lesson 5Ggplot2 Extensions and ggplotly
Chapter 9 : Speeding Up R Code
Lesson 1Implementing Best Practices to Speed Up R Code
Lesson 2Implementing Parallel Computing with doParallel and foreach
Lesson 3Writing Readable and Fast R Code with Pipes and DPlyR
Lesson 4Writing Super Fast R Code with Minimal Keystrokes Using Data.Table
Lesson 5Interface C++ in R with RCpp
Chapter 10 : Build Packages and Submit to CRAN
Lesson 1Understanding the Structure of an R Package
Lesson 2Build, Document, and Host an R Package on GitHub
Lesson 3Performing Important Checks Before Submitting to CRAN
Lesson 4Submitting an R Package to CRAN

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