Mastering R Programming
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Mastering R Programming

Build R packages, gain in-depth knowledge of machine learning, and master advanced programming techniques in R
3.8 (6 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
106 students enrolled
Created by Packt Publishing
Last updated 12/2016
English
Current price: $10 Original price: $110 Discount: 91% off
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Includes:
  • 5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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
View Curriculum
Requirements
  • Basic knowledge of R would be helpful.It assumes you are somewhat familiar 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
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.

About The Author

Selva 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.

Who is the target audience?
  • The video is for machine learning engineers, statisticians, and data scientists.
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Curriculum For This Course
54 Lectures
05:12:00
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Pre-Model Building Steps
5 Lectures 26:07

In this video, we will take a look at how to perform univariate analysis.

Performing Univariate Analysis
05:22

The goal of this video is to perform bivariate analysis in R using three cases.

Bivariate Analysis – Correlation, Chi-Sq Test, and ANOVA
05:42

In this video, we will see how to detect and treat outliers.

Detecting and Treating Outlier
03:20

The goal of this video is to see how to treat missing values in R.

Treating Missing Values with `mice`
03:59
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Regression Modelling - In Depth
6 Lectures 28:46

In this video we'll see what is linear regression, its purpose, when to use it, and how to implement in R.

Preview 07:35

We'll see how to interpret regression results and Interaction effects in this video

Interpreting Regression Results and Interactions Terms
05:19

In this video we will discuss what is residual analysis and detect multivariate outliers using Cook's Distance

Performing Residual Analysis and Extracting Extreme Observations With Cook's Dis
03:25

The goal of this video is to understand how to do model selection and comparison using best subsets, stepwise regression and ANOVA.

Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA
04:39

In this video we will see how to do k-fold cross validation in R.

Validating Model Performance on New Data with k-Fold Cross Validation
02:29

The goal of this video is check out how to build non-linear regression models using Splines and GAMs.

Building Non-Linear Regressors with Splines and GAMs
05:19
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Classification Models and caret Package - In Depth
6 Lectures 49:04

Our goal in this video would be to understand logistic regression, evaluation metrics of binary classification problems, and interpretation of the ROC curve.

Preview 12:38

In this video, we will understand the concept and working of naïve Bayes classifier and how to implement the R code.

Understanding the Concept and Building Naive Bayes Classifier
09:23

In this video, we will look at what k-nearest neighbors algorithms, how does it works and how to implement it in T.

Building k-Nearest Neighbors Classifier
07:01

The goal of this video is to understand how decision trees work, what they are used for, and how to implement then.

Building Tree Based Models Using RPart, cTree, and C5.0
06:32

The goal of this video is know what the various features of the caret package are and how to build predictive models.

Building Predictive Models with the caret Package
08:11

The goal of this video is to know how to do feature selection before building predictive models.

Selecting Important Features with RFE, varImp, and Boruta
05:19
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Core Machine Learning - In Depth
5 Lectures 37:29

In this video, we will look at how support vector machines work.

Preview 08:03

In this video, we will look at the concept behind bagging and random forests and how to implement it to solve problems.

Understanding Bagging and Building Random Forest Classifier
05:06

Let's understand what boosting is and how stochastic gradient boosting works with GBM.

Implementing Stochastic Gradient Boosting with GBM
05:18

In this video, we will look at what regularization is, ridge and lasso regression, and how to implement it.

Regularization with Ridge, Lasso, and Elasticnet
08:52

Let's look at how XG Boost works and how to implement it in this video.

Building Classifiers and Regressors with XGBoost
10:10
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Unsupervised Learning
6 Lectures 30:44

Our goal in this video would be to reduce the dimensionality of data with principal components, and understand the concept and how to implement it in R.

Preview 05:04

In this video, we will understand the k-means clustering algorithm and implement it using the principal components.

Clustering with k-means and Principal Components
03:16

In this video, we will analyze the clustering tendency of a dataset and identify the ideal number of clusters or groups.

Determining Optimum Number of Clusters
05:24

The goal of this video is to understand the logic of hierarchical clustering, types, and how to implement it in R.

Understanding and Implementing Hierarchical Clustering
02:36

How to use affinity propagation to cluster data points? How is it different from conventional algorithms?

Clustering with Affinity Propagation
05:24

How to build recommendation engines to recommend products/movies to new and existing users?

Building Recommendation Engines
09:00
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Time Series Analysis and Forecasting
6 Lectures 24:50

The goal of this video is to understand what a time series is, how to create time series of various frequencies, and the enhanced facilities available in the xts package.

Preview 05:41

The goal of this video is to understand the characteristics of a time series: stationarity and how to de-trend and de-seasonalize a time series.

Stationarity, De-Trend, and De-Seasonalize
04:07

In this video, we will introduce the characteristics of time series such as ACF, PACF, and CCF; why they matter; and how to interpret them.

Understanding the Significance of Lags, ACF, PACF, and CCF
03:49

Our goal in this video would be to understand moving average and exponential smoothing and use it to forecast.

Forecasting with Moving Average and Exponential Smoothing
02:25

In this video, we will understand how double exponential smoothing and holt winter forecasting works, when to use them, and how to implement them in R.

Forecasting with Double Exponential and Holt Winters
03:22

Let's look at what ARIMA forecasting is, understand the concepts, and learn how ARIMA modelling works in this video.

Forecasting with ARIMA Modelling
05:26
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Text Analytics - In Depth
6 Lectures 39:17

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.

Preview 09:24

Our goal in this video is to know how to process texts using tm package and understand the significance of TF-IDF and its implementation. Finally, we see how to draw a word cloud in R.

Corpus, TDM, TF-IDF, and Word Cloud
09:06

Let's see how to use cosine similarity and latent semantic analysis to find and map similar documents.

Cosine Similarity and Latent Semantic Analysis
07:20

In this video, we will see how to extract the underlying topics in a document, the keywords related to each topic and the proportion of topics in each document.

Extracting Topics with Latent Dirichlet Allocation
05:07

Let's check out how to perform sentiment analysis and scoring in R.

Sentiment Scoring with tidytext and Syuzhet
04:23

How to classify texts with machine learning algorithms using the RTextTools package?

Classifying Texts with RTextTools
03:57
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ggplot2 - Core Knowledge
5 Lectures 22:43

The goal of this videos is to understand what is the basic structure of to make charts with ggplot, how to customize the aesthetics, and manipulate the theme elements.

Preview 07:18

In this video, we will see how to manipulate the legend the way we want and how to add texts and annotation in ggplot.

Manipulating Legend, AddingText, and Annotation
03:31

The goal of this video is to understand how to plot multiple plots in the same chart and how to change the layouts of ggplot.

Drawing Multiple Plots with Faceting and Changing Layouts
03:18

How to make various types of plots in ggplot such as bar chart, time series, boxplot, ribbon chart,and so on.

Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots
05:25

In this video, we will understand what the popular ggplot extensions are, and where to find them, and their applications.

ggplot2 Extensions and ggplotly
03:11
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Speeding Up R Code
5 Lectures 33:34

We will discuss the best practices that should be followed to minimize code runtime in this video.

Preview 05:46

Let's tackle the implementation of parallel computing in R.

Implementing Parallel Computing with doParallel and foreach
04:22

The goal of this video is understand how to work with DplyR and pipes.

Writing Readable and Fast R Code with Pipes and DPlyR
05:39

In this video, we will discuss how to manipulate data with the data.table package, how to achieve maximum speed, and what the various features of data.table are.

Writing Super Fast R Code with Minimal Keystrokes Using Data.Table
06:38

Our main focus in this video is to understand how to write C++ code and make it work in R. Also leverage the speed of C++ in R, interface Rcpp with R, and write Rcpp code.

Interface C++ in R with RCpp
11:09
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Build Packages and Submit to CRAN
4 Lectures 19:26

We'll take a look at the components of an R package in this video.

Preview 05:02

In this video, we will look at how to create an R Package so that it can be submitted to CRAN.

Build, Document, and Host an R Package on GitHub
07:09

We will understand the mandatory checks and common problems faced by developers when creating R packages in this video.

Performing Important Checks Before Submitting to CRAN
04:05

The goal of this video is to show how to submit an R package to CRAN.

Submitting an R Package to CRAN
03:10
About the Instructor
Packt Publishing
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