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Supervised Learning

Regression, classification, decision trees, random forests, SVM.

Course Duration: 8 hours​

What You'll Learn​

  • Linear and logistic regression
  • Decision trees and random forests
  • Support Vector Machines
  • Model evaluation metrics
  • Cross-validation techniques
  • Hyperparameter tuning

Prerequisites​

  • Python for AI
  • Mathematics for AI basics

Course Modules​

  1. Introduction to Supervised Learning
  2. Linear Regression
  3. Polynomial Regression
  4. Logistic Regression
  5. Decision Trees
  6. Random Forests
  7. Gradient Boosting (XGBoost, LightGBM)
  8. Support Vector Machines
  9. Model Evaluation Metrics
  10. Cross-Validation
  11. Hyperparameter Tuning
  12. Project: End-to-end ML Pipeline

Algorithms Covered​

  • Linear/Logistic Regression
  • Decision Tree / Random Forest
  • XGBoost / LightGBM
  • SVM / Kernel SVM
  • K-Nearest Neighbors