![Mastering R Programming [Video]](https://static.packt-cdn.com/products/9781786464781/cover/smaller)
Mastering R Programming [Video]
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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`
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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
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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
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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
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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
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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
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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
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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
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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
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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