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.
In this video, we will take a look at how to perform univariate analysis.
The goal of this video is to perform bivariate analysis in R using three cases.
In this video, we will see how to detect and treat outliers.
The goal of this video is to see how to treat missing values in R.
In this video we'll see what is linear regression, its purpose, when to use it, and how to implement in R.
We'll see how to interpret regression results and Interaction effects in this video
In this video we will discuss what is residual analysis and detect multivariate outliers using Cook's Distance
The goal of this video is to understand how to do model selection and comparison using best subsets, stepwise regression and ANOVA.
In this video we will see how to do k-fold cross validation in R.
The goal of this video is check out how to build non-linear regression models using Splines and GAMs.
Our goal in this video would be to understand logistic regression, evaluation metrics of binary classification problems, and interpretation of the ROC curve.
In this video, we will understand the concept and working of naïve Bayes classifier and how to implement the R code.
In this video, we will look at what k-nearest neighbors algorithms, how does it works and how to implement it in T.
The goal of this video is to understand how decision trees work, what they are used for, and how to implement then.
The goal of this video is know what the various features of the caret package are and how to build predictive models.
The goal of this video is to know how to do feature selection before building predictive models.
In this video, we will look at how support vector machines work.
In this video, we will look at the concept behind bagging and random forests and how to implement it to solve problems.
Let's understand what boosting is and how stochastic gradient boosting works with GBM.
In this video, we will look at what regularization is, ridge and lasso regression, and how to implement it.
Let's look at how XG Boost works and how to implement it in this video.
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.
In this video, we will understand the k-means clustering algorithm and implement it using the principal components.
In this video, we will analyze the clustering tendency of a dataset and identify the ideal number of clusters or groups.
The goal of this video is to understand the logic of hierarchical clustering, types, and how to implement it in R.
How to use affinity propagation to cluster data points? How is it different from conventional algorithms?
How to build recommendation engines to recommend products/movies to new and existing users?
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.
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.
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.
Our goal in this video would be to understand moving average and exponential smoothing and use it to forecast.
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.
Let's look at what ARIMA forecasting is, understand the concepts, and learn how ARIMA modelling works in this video.
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.
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.
Let's see how to use cosine similarity and latent semantic analysis to find and map similar documents.
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.
Let's check out how to perform sentiment analysis and scoring in R.
How to classify texts with machine learning algorithms using the RTextTools package?
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.
In this video, we will see how to manipulate the legend the way we want and how to add texts and annotation in ggplot.
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.
How to make various types of plots in ggplot such as bar chart, time series, boxplot, ribbon chart,and so on.
In this video, we will understand what the popular ggplot extensions are, and where to find them, and their applications.
We will discuss the best practices that should be followed to minimize code runtime in this video.
Let's tackle the implementation of parallel computing in R.
The goal of this video is understand how to work with DplyR and pipes.
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.
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.
We'll take a look at the components of an R package in this video.
In this video, we will look at how to create an R Package so that it can be submitted to CRAN.
We will understand the mandatory checks and common problems faced by developers when creating R packages in this video.
The goal of this video is to show how to submit an R package to CRAN.
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