Supervised Learning

UNIT 1: Introduction to Supervised Learning

1.1    Introduction to Data Science

1.2    What is Data?

1.3    Importance and applications of Data Science

1.4    Key Components of Data Science: Data Collection, Cleaning, Analysis and visualization

1.5    Difference between Data Science and Data Analytics

UNIT 2: Linear Regression

2.1 Regression basics: Relationship between attributes using Covariance and Correlation

2.2 Relationship between multiple variables: Regression (Linear, Multivariate) in prediction.

2.3 Residual Analysis

2.4 Identifying significant features, feature reduction using AIC, multi-collinearity

2.5 Non-normality and Heteroscedasticity

2.6 Hypothesis testing of Regression Model

2.7 Confidence intervals of Slope

UNIT 3:  Multiple Linear Regression

3.1 Polynomial Regression

3.2 Regularization methods

3.3 Lasso, Ridge and Elastic nets

3.4 Categorical Variables in Regression

UNIT 4:  Non-Linear Regression

4.1 Logit function and interpretation

4.2 Types of error measures (ROCR)

4.3 Logistic Regression in classification

UNIT 5: Classification

5.1    Basic Concepts

5.2    Decision Tree Induction

5.3    Bayes Classification Methods

5.4    Introducing Ensemble methods

5.5    Support Vector Machine

UNIT 6:  Clustering

6.1 Distance measures

6.2 Different clustering methods (Distance, Density, Hierarchical)

6.3 Iterative distance-based clustering;

6.4 Dealing with continuous, categorical values in K-Means

6.5 Constructing a hierarchical cluster