Books & Videos

Table of Contents

Chapter: Pre-Model Building Steps

The Course Overview

07m 44s

Performing Univariate Analysis

05m 22s

Bivariate Analysis – Correlation, Chi-Sq Test, and ANOVA

05m 42s

Detecting and Treating Outlier

03m 20s

Treating Missing Values with `mice`

03m 59s

Chapter: Regression Modelling - In Depth

Building Linear Regressors

07m 35s

Interpreting Regression Results and Interactions Terms

05m 19s

Performing Residual Analysis and Extracting Extreme Observations With Cook's Distance

03m 25s

Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA

04m 39s

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

02m 29s

Building Non-Linear Regressors with Splines and GAMs

05m 19s

Chapter: Classification Models and caret Package - In Depth

Building Logistic Regressors, Evaluation Metrics, and ROC Curve

12m 38s

Understanding the Concept and Building Naive Bayes Classifier

09m 23s

Building k-Nearest Neighbors Classifier

07m 1s

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

06m 32s

Building Predictive Models with the caret Package

08m 11s

Selecting Important Features with RFE, varImp, and Boruta

05m 19s

Chapter: Core Machine Learning - In Depth

Building Classifiers with Support Vector Machines

08m 3s

Understanding Bagging and Building Random Forest Classifier

05m 6s

Implementing Stochastic Gradient Boosting with GBM

05m 18s

Regularization with Ridge, Lasso, and Elasticnet

08m 52s

Building Classifiers and Regressors with XGBoost

10m 10s

Chapter: Unsupervised Learning

Dimensionality Reduction with Principal Component Analysis

05m 4s

Clustering with k-means and Principal Components

03m 16s

Determining Optimum Number of Clusters

05m 24s

Understanding and Implementing Hierarchical Clustering

02m 36s

Clustering with Affinity Propagation

05m 24s

Building Recommendation Engines

09m 0s

Chapter: Time Series Analysis and Forecasting

Understanding the Components of a Time Series, and the xts Package

05m 41s

Stationarity, De-Trend, and De-Seasonalize

04m 7s

Understanding the Significance of Lags, ACF, PACF, and CCF

03m 49s

Forecasting with Moving Average and Exponential Smoothing

02m 25s

Forecasting with Double Exponential and Holt Winters

03m 22s

Forecasting with ARIMA Modelling

05m 26s

Chapter: Text Analytics - In Depth

Scraping Web Pages and Processing Texts

09m 24s

Corpus, TDM, TF-IDF, and Word Cloud

09m 6s

Cosine Similarity and Latent Semantic Analysis

07m 20s

Extracting Topics with Latent Dirichlet Allocation

05m 7s

Sentiment Scoring with tidytext and Syuzhet

04m 23s

Classifying Texts with RTextTools

03m 57s

Chapter: ggplot2 - Core Knowledge

Building a Basic ggplot2 and Customizing the Aesthetics and Themes

07m 18s

Manipulating Legend, AddingText, and Annotation

03m 31s

Drawing Multiple Plots with Faceting and Changing Layouts

03m 18s

Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots

05m 25s

ggplot2 Extensions and ggplotly

03m 11s

Chapter: Speeding Up R Code

Implementing Best Practices to Speed Up R Code

05m 46s

Implementing Parallel Computing with doParallel and foreach

04m 22s

Writing Readable and Fast R Code with Pipes and DPlyR

05m 39s

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

06m 38s

Interface C++ in R with RCpp

11m 9s

Chapter: Build Packages and Submit to CRAN

Understanding the Structure of an R Package

05m 2s

Build, Document, and Host an R Package on GitHub

07m 10s

Performing Important Checks Before Submitting to CRAN

04m 5s

Submitting an R Package to CRAN

03m 11s