Mastering R Programming [Video]

Mastering R Programming [Video]

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Selva Prabhakaran

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

ISBN 139781786464781
Course Length5 hours 12 minutes

Video 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 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.

Style and Approach

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.

Table of Contents

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

What You Will Learn

  • 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

Authors

Table of Contents

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

Video Details

ISBN 139781786464781
Course Length5 hours 12 minutes
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