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Machine Learning Syllabus Overview
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Module 6
Tree-Based Ensemble Models
Decision Tree Splits: Intuitive Gini & Best Feature Selection
Explain intuitively how a decision tree selects the best feature to split on using criteria like Gini impurity.
Decision Trees with Scikit-learn
Train and visualize a decision tree classifier using scikit-learn.
3
Understanding Overfitting and Pruning in Decision Trees
Describe overfitting in decision trees and how parameters like 'max_depth' can be used for pruning.
4
Understanding Bagging: Reducing Variance with Ensemble Learning
Explain the concept of bootstrap aggregating (bagging) as an ensemble technique to reduce variance.
5
Random Forests: Beyond Single Trees
Train a Random Forest classifier and explain how it improves upon a single decision tree.
Previous module
Instance-Based and Probabilistic Classification
Next module
Advanced Linear Classifiers and SVMs