Course curriculum

  • 1
    Regression I
    • Course Introduction
    • Resources for this Course
    • Introduction to Linear Regression
    • Significance of Slope and Intercept in the linear regression
    • How Model Decides The Best-Fit Line
    • Let’s Build a Simple Linear Regression Model
    • Model Understanding Using Descriptive Approach
    • Model Understanding Using Descriptive Approach - II
    • Model Building Using Predictive Approach
    • Quiz: Linear regression
  • 2
    Regression II
    • Introduction to Logistic Regression
    • Lines to Curves with Logistic Regression
    • Reading Between the Curves with Log Loss
    • Stats Model Summary
    • Feature Selection and Scaling
    • Predictive model in Logistic Regression
    • Quiz: Logistic regression
    • Generalized Linear Models
    • Assumptions of Linear Regression
  • 3
    Project
    • Project: Healthcare
  • 4
    Decision Tree
    • Introduction to Decision Trees
    • Let’s Visualize The Decision Tree
    • How Do Decision Trees Decide
    • How Decision Trees Make Predictions
    • Hands on Building the Decision Tree Classification Model- Part 1
    • Hyperparameters of Decision Trees
    • Hands on Building the Decision Tree Classification Model - Part 2
    • Building a Decision Tree Regression Model Hands on
    • Handling Imbalanced Datasets
    • Handling Imbalanced Datasets - Hands on
    • Quiz: Decision Trees
    • Project: Building Decision Tree Model For Anova Insurance
  • 5
    Regularization
    • Division of the dataset
    • Overfitting and Underfitting
    • Introduction To The Apex Dataset
    • L1 Regularization - Linear Regression
    • L2 Regularization - Linear Regression
    • Elastic Net Regularization - Linear Regression
    • Quiz: Regularization in Linear Regression
    • Fine-Tuning Logistic Regression
    • L1 Regularization - Logistic Regression
    • L2 Regularization - Logistic Regression
    • Elastic Net Regularization - Logistic Regression
    • Quiz: Regularization in Logistic Regression
    • Projects: Employee turnover
  • 6
    NYC Taxi trip project
    • Exploring the NYC Dataset
    • Project: NYC taxi trip duration prediction
    • Course Conclusion