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Module 8
Advanced Supervised Learning Models
1
Multiple Linear Regression: Implementation and Interpretation
Implement multiple linear regression and interpret model coefficients.
2
Regularization: Preventing Overfitting with Ridge and Lasso
Apply regularization techniques (Ridge and Lasso regression) to prevent overfitting.
3
Binary Classification with Logistic Regression
Train a logistic regression classifier for binary classification tasks.
4
Classification Model Evaluation Metrics
Evaluate a classification model using accuracy, precision, recall, and F1-score.
5
Understanding the Confusion Matrix
Interpret a confusion matrix for a classification problem.
6
Understanding and Implementing K-Nearest Neighbors (KNN)
Explain the K-Nearest Neighbors (KNN) algorithm and train a KNN classifier.
Decision Trees: Prediction & Training
Explain how a Decision Tree makes predictions and train a classifier.
8
Random Forests: Ensemble Power for Classification
Train a Random Forest classifier and explain how it improves upon a single Decision Tree.
Previous module
Foundations of Classical Machine Learning
Next module
Model Tuning and Unsupervised Learning