Course curriculum

  1. 1

    • Google Subtitles Instructions

    • 1. Simple Linear Regression - Theory

    • 2. Simple Linear Regression - Code
  2. 2

    • 1. Multiple Linear Regression - Theory

    • 2. Multiple Linear Regression - Code

    • 3. Assumptions of Linear Regression - Theory

    • 4. Curse of Dimensionality - Theory

    • 5. Model Accuracy and Train Test Split - Code
  3. 3

    • 1. Logistic Regression - Theory

    • 2. Logistic Regression - Code
  4. 4

    • 1. Decision Tree - Theory

    • 2. Cross-Validation & Hyperparameter Tuning - Theory

    • 3. Decision Tree Classifier - Code

    • 4. Decision Tree Classifier with Hyperparameter Tuning - Code

    • 5. Decision Tree Regressor - Code

    • 6. Decision Tree Regressor with Hyperparameter Tuning - Code
  5. 5

    • 1. Random Forest - Theory

    • 2. Random Forest Classifier - Code

    • 3. Random Forest Classifier with Hyperparameter Tuning - Code

    • 4. Random Forest Regressor - Code

    • 5. Random Forest Regressor with Hyperparameter Tuning - Code
  6. 6

    • 1. Ensembling Models - Theory

    • 2. Model Ensembling - Code

    • 3. Unsupervised vs. Supervised Learning + Summary of NLP and Deep Learning - Theory
  7. 7

    • 1. Stepwise Selection - Theory

    • 2. Stepwise Selection - Code

    • 3. Label Encoding - Theory

    • 4. Label Encoding - Code

    • 5. Dummification - Theory

    • 6. Dummification - Code

    • 7. KNN - Theory

    • 8. KNN - Code

    • 9. Data Cleaning Walkthrough 1 (Housing Prices) - Code

    • 10. Data Cleaning Walkthrough 2 (Titanic) - Code
  8. 8

    • 1. Simple Linear Regression in R - Code

    • 2. Multiple Linear Regression in R - Code

    • 3. Logistic Regression in R - Code

    • 4. Decision Tree in R - Code

    • 5. Random Forest in R - Code

    • 6. R Data Cleaning Example Part 1 - Code

    • 7. R Data Cleaning Example Part 2 - Code