Machine Learning

Linear regression: Solving regression problems
Introduction – Applications
Assumptions of Linear Regression
Building Linear Regression Model
Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
Assess the overall effectiveness of the model
Interpretation of Results

Logistic regression: Solving classification problems

Introduction – Applications
Building Logistic Regression Model
Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
Validation of Logistic Regression Models
Standard Business Outputs (ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)

Supervised learning: decision trees
Decision Trees – Introduction – Applications
Types of Decision Tree Algorithms
Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
Pruning a Decision Tree; Cost as a consideration
Decision Trees – Validation
Overfitting – Best Practices to avoid

Unsupervised learning : Segmentation
What is segmentation & Role of ML in Segmentation?
K-Means Clustering
Expectation Maximization
Principle component Analysis (PCA)

Times series forecasting: solving forecasting problems
Introduction – Applications
Basic Techniques – Averages, Smoothening, etc
Advanced Techniques – AR Models, ARIMA, etc
Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

Supervised learning: Naive bayes
Concept of Conditional Probability
Bayes Theorem and Its Applications
Naïve Bayes for classification
Applications of Naïve Bayes in Classifications

Supervised learning : KNN
What is KNN & Applications?
KNN for missing treatment
KNN For solving regression problems
KNN for solving classification problems
Validating KNN model
Model fine tuning with hyper parameters

Supervised learning: Ensemble learning
Concept of Ensembling
Random forest (Logic, Practical Applications)

Supervised learning: support vector machines
Motivation for Support Vector Machine & Applications
Interpretation of Outputs and Fine tune the models with hyper parameters