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Machine Learning Syllabus Overview
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Module 5
Instance-Based and Probabilistic Classification
1
K-NN: Distance-Based Classification
Explain the K-Nearest Neighbors (K-NN) algorithm's distance-based voting mechanism for classification.
2
K-NN with scikit-learn: Exploring the Impact of K
Apply K-NN using scikit-learn and analyze the effect of changing the number of neighbors (K).
Generative vs. Discriminative Classifiers
Distinguish between generative and discriminative classification models.
Understanding Bayes' Theorem: Updating Beliefs
Explain the intuition behind Bayes' theorem for updating beliefs with new evidence.
Naive Bayes: Understanding Conditional Independence
Describe the 'naive' conditional independence assumption in Naive Bayes and its practical benefits.
6
Training Gaussian Naive Bayes with scikit-learn
Train a Gaussian Naive Bayes classifier using scikit-learn on a dataset.
Previous module
Linear Regression and Regularization
Next module
Tree-Based Ensemble Models