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Mastering R Programming

Video Description

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

About This Video

  • 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.

  • In Detail

    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

    Table of Contents

    1. Chapter 1 : Pre-Model Building Steps
      1. The Course Overview 00:07:45
      2. Performing Univariate Analysis 00:05:22
      3. Bivariate Analysis – Correlation, Chi-Sq Test, and ANOVA 00:05:43
      4. Detecting and Treating Outlier 00:03:21
      5. Treating Missing Values with `mice` 00:03:59
    2. Chapter 2 : Regression Modelling-In Depth
      1. Building Linear Regressors 00:07:35
      2. Interpreting Regression Results and Interactions Terms 00:05:19
      3. Performing Residual Analysis and Extracting Extreme Observations With Cook's Distance 00:03:25
      4. Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA 00:04:39
      5. Validating Model Performance on New Data with k-Fold Cross Validation 00:02:29
      6. Building Non-Linear Regressors with Splines and GAMs 00:05:20
    3. Chapter 3 : Classification Models and caret Package-In Depth
      1. Building Logistic Regressors, Evaluation Metrics, and ROC Curve 00:12:38
      2. Understanding the Concept and Building Naive Bayes Classifier 00:09:24
      3. Building k-Nearest Neighbors Classifier 00:07:01
      4. Building Tree Based Models Using RPart, cTree, and C5.0 00:06:33
      5. Building Predictive Models with the caret Package 00:08:11
      6. Selecting Important Features with RFE, varImp, and Boruta 00:05:19
    4. Chapter 4 : Core Machine Learning-In Depth
      1. Building Classifiers with Support Vector Machines 00:08:04
      2. Understanding Bagging and Building Random Forest Classifier 00:05:07
      3. Implementing Stochastic Gradient Boosting with GBM 00:05:18
      4. Regularization with Ridge, Lasso, and Elasticnet 00:08:53
      5. Building Classifiers and Regressors with XGBoost 00:10:10
    5. Chapter 5 : Unsupervised Learning
      1. Dimensionality Reduction with Principal Component Analysis 00:05:05
      2. Clustering with k-means and Principal Components 00:03:16
      3. Determining Optimum Number of Clusters 00:05:25
      4. Understanding and Implementing Hierarchical Clustering 00:02:36
      5. Clustering with Affinity Propagation 00:05:25
      6. Building Recommendation Engines 00:09:01
    6. Chapter 6 : Time Series Analysis and Forecasting
      1. Understanding the Components of a Time Series, and the xts Package 00:05:42
      2. Stationarity, De-Trend, and De-Seasonalize 00:04:07
      3. Understanding the Significance of Lags, ACF, PACF, and CCF 00:03:49
      4. Forecasting with Moving Average and Exponential Smoothing 00:02:25
      5. Forecasting with Double Exponential and Holt Winters 00:03:23
      6. Forecasting with ARIMA Modelling 00:05:26
    7. Chapter 7 : Text Analytics-In Depth
      1. Scraping Web Pages and Processing Texts 00:09:24
      2. In this video, we'll take a look at how to scrape data from web pages and how to clean and process raw web and other textual data. 00:09:07
      3. Cosine Similarity and Latent Semantic Analysis 00:07:20
      4. Extracting Topics with Latent Dirichlet Allocation 00:05:07
      5. Sentiment Scoring with tidytext and Syuzhet 00:04:23
      6. Classifying Texts with RTextTools 00:03:57
    8. Chapter 8 : ggplot2
      1. Building a Basic ggplot2 and Customizing the Aesthetics and Themes 00:07:18
      2. Manipulating Legend, AddingText, and Annotation 00:03:31
      3. Drawing Multiple Plots with Faceting and Changing Layouts 00:03:18
      4. Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots 00:05:25
      5. ggplot2 Extensions and ggplotly 00:03:11
    9. Chapter 9 : Speeding Up R Code
      1. Implementing Best Practices to Speed Up R Code 00:05:47
      2. Implementing Parallel Computing with doParallel and foreach 00:04:22
      3. Writing Readable and Fast R Code with Pipes and DPlyR 00:05:40
      4. Writing Super Fast R Code with Minimal Keystrokes Using Data.Table 00:06:38
      5. Interface C++ in R with RCpp 00:11:09
    10. Chapter 10 : Build Packages and Submit to CRAN
      1. Understanding the Structure of an R Package 00:05:02
      2. Build, Document, and Host an R Package on GitHub 00:07:10
      3. Performing Important Checks Before Submitting to CRAN 00:04:06
      4. Submitting an R Package to CRAN 00:03:11