Supervised and Unsupervised Learning With R Programming Language
Harness The Power Of Machine Learning For Unsupervised & Supervised Learning In R -- With Practical Examples
Segment - 01 - Installing R and R Studio
Segment - 02 - Read in CSV & Excel Data
Segment - 03 - Read in Unzipped Folder
Segment - 04 - Read in Online CSV
Segment - 05 - Read in Google Sheets
Segment - 06 - Read in Data from Online HTML Tables-Part 1
Segment - 07 - Read in Data from Online HTML Tables-Part 2
Segment - 08 - Read Data from a Database
Segment - 09 - Remove Missing Values
Segment - 10 - Introduction to dplyr for Data Summarizing-Part 1
Segment - 11 - Introduction to dplyr for Data Summarizing-Part 2
Segment - 12 - Exploratory Data Analysis(EDA): Basic Visualizations with R
Segment - 13 - More Exploratory Data Analysis with xda
Segment - 14 - Data Exploration & Visualization With dplyr & ggplot2
Segment - 15 - Testing for Correlation
Segment - 16 - Chi Square Test
Segment - 17 - How is Machine Learning Different from Statistical Data Analysis?
Segment - 18 - What is Machine Learning (ML) About?
Segment - 19 - K-Means Theory
Segment - 20 - Other Ways of Selecting Cluster Numbers
Segment - 21 - Fuzzy K-Means Clustering
Segment - 22 - Weighted k-means
Segment - 23 - Hierarchical Clustering in R
Segment - 24 - Expectation-Maximization (EM) in R
Segment - 25 - DBSCAN Clustering in R
Segment - 26 - Cluster a Mixed Dataset
Segment - 27 - Should We Even Do Clustering?
Segment - 28 - Introduction
Segment - 29 - Principal Component Analysis (PCA)
Segment - 30 - More on PCA
Segment - 31 - Multidimensional Scaling
Segment - 32 - Singular Value Decomposition (SVD)