Course curriculum

  • 1
    Bagging
    • Resources to be used in this course
    • Course Introduction
    • Understanding Ensemble Learning
    • Introducing Bagging Algorithms
    • Hands-on to Bagging Meta Estimator
    • Introduction to Random Forest
    • Understanding Out-Of-Bag Score
    • Random Forest VS Classical Bagging VS Decision Tree
    • Project
  • 2
    Boosting
    • Introduction to Boosting
    • AdaBoost Step-by-Step Explanation
    • Hands-on - AdaBoost
    • Gradient Boosting Machines (GBM)
    • Hands-on Gradient Boost
    • Other Algo (XGBoost, LightBoost. CatBoost)
    • Project: Anova Insurance
  • 3
    Feature Engineering
    • Introduction to Feature Engineering and Hyperparameter Tuning
    • Spliting the dataset
    • Feature Transformation
    • Feature Generation
    • Feature Selection
  • 4
    Hyperparameter Tuning
    • Introduction to Hyperparameter and Grid SearchCV
    • Grid Search CV
    • Random Search CV
    • Bayesian Optimization
    • Bayesian Optimization in synergix dataset
    • Project: Rain Tomorrow in Australia
  • 5
    Combining Models
    • Introduction to the module
    • Understanding Voting
    • Coding - Voting
    • Understanding Stacking
    • Understanding Hold out Method/Blending
    • Project: Stacking
  • 6
    Model Selection
    • The Stepping Stones in Model Selection
    • Factors to Consider While Selecting a Model