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Learning Path: R: Real-World Data Mining With R

Video Introducing this tutorial

Chapter 1 : Learning Data Mining with R
The Course Overview 00:03:31
Getting Started with R 00:05:06
Data Preparation and Data Cleansing 00:04:10
The Basic Concepts of R 00:05:46
Data Frames and Data Manipulation 00:05:29
Data Points and Distances in a Multidimensional Vector Space 00:03:59
An Algorithmic Approach to Find Hidden Patterns in Data 00:06:24
A Real-world Life Science Example 00:04:29
Example – Using a Single Line of Code in R 00:04:00
R Data Types 00:05:44
R Functions and Indexing 00:04:15
S3 Versus S4 – Object-oriented Programming in R 00:04:45
Market Basket Analysis 00:03:01
Introduction to Graphs 00:02:09
Different Association Types 00:05:27
The Apriori Algorithm 00:06:38
The Eclat Algorithm 00:03:54
The FP-Growth Algorithm 00:03:48
Mathematical Foundations 00:06:01
The Naive Bayes Classifier 00:03:50
Spam Classification with Naïve Bayes 00:03:33
Support Vector Machines 00:04:29
K-nearest Neighbors 00:03:21
Hierarchical Clustering 00:05:45
Distribution-based Clustering 00:06:55
Density-based Clustering 00:03:12
Using DBSCAN to Cluster Flowers Based on Spatial Properties 00:02:25
Introduction to Neural Networks and Deep Learning 00:06:09
Using the H2O Deep Learning Framework 00:02:28
Real-time Cloud Based IoT Sensor Data Analysis 00:06:17


Chapter 2 : R Data Mining Projects
The Course Overview 00:03:53
What Is Data Mining? 00:04:58
Introduction to the R Programming Language 00:14:44
Data Type Conversion 00:02:11
Sorting, Merging, Indexing, and Subsetting Dataframes 00:09:46
Date and Time Formatting 00:03:02
Types of Functions 00:02:24
Loop Concepts 00:02:30
Applying Concepts 00:03:18
String Manipulation 00:02:15
NA and Missing Value Management and Imputation Techniques 00:02:52
Univariate Data Analysis 00:09:19
Bivariate Analysis 00:01:49
Multivariate Analysis 00:00:58
Understanding Distributions and Transformation 00:04:54
Interpreting Distributions and Variable Binning 00:05:15
Contingency Tables, Bivariate Statistics, and Checking for Data Normality 00:06:17
Hypothesis Testing 00:11:59
Non-Parametric Methods 00:02:37
Introduction to Data Visualization 00:16:07
Visualizing Charts, and Geo Mapping 00:03:39
Visualizing Scatterplot, Word Cloud and More 00:10:51
Using plotly 00:04:50
Creating Geo Mapping 00:02:21
Introduction about Regression 00:04:09
Linear Regression 00:14:04
Stepwise Regression Method for Variable Selection 00:02:20
Logistic Regression 00:09:39
Cubic Regression 00:08:47
Introduction to Market Basket Analysis 00:12:29
Practical project 00:15:39


Chapter 3 : Advanced Data Mining projects with R
The Course Overview 00:03:53
Understanding Customer Segmentation 00:03:50
Clustering Methods – K means and Hierarchical 00:15:37
Clustering Methods – Model Based, Other and Comparison 00:05:33
What Is Recommendation? 00:07:29
Application of Methods and Limitations of Collaborative Filtering 00:02:31
Practical Project 00:04:41
Why Dimensionality Reduction? 00:09:14
Practical Project around Dimensionality Reduction 00:12:41
Parametric Approach to Dimension Reduction 00:02:42
Introduction to Neural Networks 00:04:07
Understanding the Math Behind the Neural Network 00:01:59
Neural Network Implementation in R 00:01:59
Neural Networks for Prediction 00:03:25
Neural Networks for Classification 00:01:32
Neural Networks for Forecasting 00:01:16
Merits and Demerits of Neural Networks 00:02:21

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