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Learning Data Mining with R
Getting Started – A Motivating Example
The Course Overview (3:30)
Getting Started with R (5:05)
Data Preparation and Data Cleansing (4:10)
The Basic Concepts of R (5:46)
Data Frames and Data Manipulation (5:29)
Clustering – A Dating App for Your Data Points
Data Points and Distances in a Multidimensional Vector Space (3:59)
An Algorithmic Approach to Find Hidden Patterns in Data (6:24)
A Real-world Life Science Example (4:28)
R Deep Dive, Why Is R Really Cool?
Example – Using a Single Line of Code in R (4:00)
R Data Types (5:44)
R Functions and Indexing (4:14)
S3 Versus S4 – Object-oriented Programming in R (4:44)
Association Rule Mining
Market Basket Analysis (3:00)
Introduction to Graphs (2:09)
Different Association Types (5:27)
The Apriori Algorithm (6:38)
The Eclat Algorithm (3:53)
The FP-Growth Algorithm (3:47)
Classification
Mathematical Foundations (6:00)
The Naive Bayes Classifier (3:50)
Spam Classification with Naïve Bayes (3:32)
Support Vector Machines (4:28)
K-nearest Neighbors (3:21)
Clustering
Hierarchical Clustering (5:44)
Distribution-based Clustering (6:54)
Density-based Clustering (3:11)
Using DBSCAN to Cluster Flowers Based on Spatial Properties (2:25)
Cognitive Computing and Artificial Intelligence in Data Mining
Introduction to Neural Networks and Deep Learning (6:09)
Using the H2O Deep Learning Framework (2:28)
Real-time Cloud Based IoT Sensor Data Analysis (6:17)
Hierarchical Clustering
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