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
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