• The concept of the model in analytics and how it is used?
  • Common terminology used in analytics & modeling process
  • Popular modeling algorithms
  • Different Phases of Predictive Modeling
  • Need for structured exploratory data
  • EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
  • Identify missing data
  • Identify outliers data
  • Visualize the data trends and patterns
  • Introduction – Applications
  • Assumptions of Linear Regression
  • Building a 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
  • Introduction – Applications
  • Building the 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)
  • The concept of Conditional Probability
  • Bayes Theorem and Its Applications
  • Naïve Bayes for classification
  • Applications of Naïve Bayes in Classifications
  • Introduction – Applications
  • Basic Techniques – Averages, Smoothening, etc
  • Advanced Techniques – AR Models, ARIMA, etc
  • Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc
  • Introduction – Applications
  • Basic Techniques – Averages, Smoothening, etc
  • Advanced Techniques – AR Models, ARIMA, etc
  •  Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc
  • 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
  • What is segmentation & Role of ML in Segmentation?
  • K-Means Clustering
  • Expectation Maximization
  • Principle Component Analysis (PCA)
  • Motivation for Support Vector Machine & Applications
  • Interpretation of Outputs and Fine tune the models with hyper parameters
  • What are KNN & Applications?
  • KNN for missing treatment
  • KNN For solving regression problems
  • KNN for solving classification problems
  • Validating KNN model
  • Model fine tuning with hyperparameters
  • Concept of Ensembling
  • Random forest (Logic, Practical Applications)