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Working With Classes: Classify and Cluster Data With R
Introduction to the Course
Welcome to the Course and Instructor Info
Data and Code
Install R and RStudio (6:36)
Preprocessing Data in R (17:12)
Read in Data From Different Sources
Read CSV & Excel Data (9:56)
Read in Online CSV (4:04)
Read in Googlesheet (4:03)
Read in JSON Data (5:28)
Read in Database (8:23)
Data Pre-Processing and Visualisation
Start With Data Cleaning: Remove Missing Values (17:12)
Slightly Advanced Data Cleaning (8:05)
Introduction to dplyr for data summarising- part 1 (4:44)
Use dplyr for summarising & visualisations (6:07)
Exploratory data analysis (EDA) in R (18:53)
More EDA (4:16)
Association between quantitative variables (19:50)
Testing for correlation (19:50)
Association Between Qualitative Variables (8:20)
Cramer's Test for qualitative variable (3:35)
Machine Learning for Data Science
Difference Between Machine Learning And Statistical Modelling? (5:36)
Machine Learning:Basic Theory (5:32)
Cluster Unlabeled Data in R
k-means clustering (14:31)
Hierarchical clustering (14:13)
Weighted k-means (6:04)
Fuzzy k-means (18:14)
Expectation maximisation (EM) (5:50)
DBSCAN for clustering (4:58)
Cluster a mixed dataset (4:01)
Should we even do clustering? (3:07)
Evaluate clustering accuracy (5:46)
Dimension Reduction
Theory behind dimension reduction (3:17)
Principal Component Analysis (PCA) (13:10)
More PCA (4:27)
Feature Selection: Identify the Most Important Variables
Removing Highly Correlated Predictor Variables (16:42)
Variable Selection Using LASSO Regression (3:42)
Variable Selection With FSelector (13:35)
Boruta analysis for feature selection (4:51)
Theory of Supervised Learning
Some Basic Supervised Learning Concepts (10:10)
Prepare data for ML analysis (3:31)
Work With Labelled Classes: Classification
Generalised Linear Models (GLMs) (5:25)
Logistic Regression Models as Binary Classifiers
Binary Classifier with PCA (6:29)
How Good is the Model: Evaluate Accuracy (9:42)
Accuracy of Binary Classification (8:18)
More on Binary Accuracy Measures (4:19)
Linear Discriminant Analysis (LDA) (12:55)
Our Multi-class Classification Problem (6:14)
Classification Trees (11:55)
More on Classification Tree Visualization (9:20)
Classification with Party Package (5:12)
Decision Trees (8:39)
Random Forest (RF) (8:15)
Examine Individual Variable Importance for Random Forests (3:53)
GBM Classification (4:10)
Support Vector Machines (SVM) for Classification (3:55)
More SVM for Classification (3:42)
Examine Individual Variable Importance for Random Forests
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