NOC:Predictive Modelling with Applications: Supervised and Unsupervised Learning

Lecture 1 - Introduction to Predictive Modelling

Lecture 2 - Correlation coefficient

Lecture 3 - Principal Components Analysis - 1

Lecture 4 - Principal Components Analysis - 2

Lecture 5 - Principal Components Analysis - 3

Lecture 6 - Factor Analysis - 1

Lecture 7 - Factor Analysis - 2

Lecture 8 - Factor Analysis applications with R

Lecture 9 - Introduction to Cluster Analysis

Lecture 10 - Agglomerative Hierarchical clustering 1

Lecture 11 - Hierarchical clustering

Lecture 12 - Hierarchical clustering using R examples

Lecture 13 - k-means clustering

Lecture 14 - Simple linear regression - 1

Lecture 15 - Simple linear regression - 2

Lecture 16 - Multiple linear regression - 1

Lecture 17 - Multiple linear regression - 2

Lecture 18 - Multiple linear Regression - 3

Lecture 19 - Outliers - 1

Lecture 20 - Outliers - 2

Lecture 21 - Outliers Detection

Lecture 22 - Regression Modelling with Categorical predictors using R

Lecture 23 - Modelling Non-linear and Interaction effects in Regression using R

Lecture 24 - Autocorrelation - 1

Lecture 25 - Autocorrelation - 2

Lecture 26 - Tests for Normality - 1

Lecture 27 - Tests for Normality - 2

Lecture 28 - Multicollinearity in Regression - 1

Lecture 29 - Multicollinearity in Regression - 2

Lecture 30 - Multicollinearity in Regression - 3

Lecture 31 - Heteroscedasticity - 1

Lecture 32 - Heteroscedasticity - 2

Lecture 33 - Heteroscedasticity - 3

Lecture 34 - Variable Selection and Model Evaluation

Lecture 35 - Case study

Lecture 36 - Odds Ratio

Lecture 37 - Logistic Regression - 1

Lecture 38 - Logistic Regression - 2

Lecture 39 - Regression Disconituity Design