Unit 1: What is Supervised Learning?
Unit 2: The Supervised Learning Workflow
Unit 3: Common Algorithms
Intro to Supervised Learning
A practical introduction to supervised learning, covering essential algorithms, data preparation techniques, and model evaluation methods for solving real-world problems.
Unit 1: Handling Missing Data
Unit 2: Outlier Management
Unit 3: Feature Scaling
Unit 4: Encoding Categorical Features
Unit 5: Data Splitting
Unit 1: Linear Regression in Depth
Unit 2: Polynomial Regression
Unit 3: Regularization Techniques
Unit 4: Support Vector Regression
Unit 5: Decision Tree and Random Forest Regression
Unit 1: Logistic Regression
Unit 2: K-Nearest Neighbors (KNN)
Unit 3: Support Vector Machines (SVM)
Unit 4: Decision Trees and Random Forests
Unit 5: Naive Bayes
Unit 1: Cross-Validation Techniques
Unit 2: Evaluation Metrics
Unit 3: Hyperparameter Tuning
Unit 4: Model Interpretation and Communication