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Machine Learning: Algorithms & Applications
·
Module 6
Foundational Classification Algorithms
1
Understanding K-Nearest Neighbors (KNN)
Explain the K-Nearest Neighbors (KNN) algorithm, including distance metrics (Euclidean, Manhattan), the role of 'K', and how predictions are made.
2
KNN: Strengths, Weaknesses, and Considerations
Identify the strengths and weaknesses of KNN, such as the curse of dimensionality, computational cost, and sensitivity to feature scaling.
3
K-Nearest Neighbors: Classification and Hyperparameter Tuning
Apply KNN to a classification dataset, tune the hyperparameter K, and evaluate performance.
4
Understanding Naive Bayes: Intuition and Assumptions
Explain the intuition behind Naive Bayes, including Bayes' theorem and the 'naive' conditional independence assumption.
5
Naive Bayes: Gaussian, Multinomial, and Bernoulli
Distinguish between Gaussian, Multinomial, and Bernoulli Naive Bayes and identify appropriate use cases for each.
6
Naive Bayes Classification: Implementation and Evaluation
Apply a Naive Bayes classifier to a text or numerical dataset and evaluate its performance.
Previous module
Logistic Regression and Classification Evaluation
Next module
Tree-Based Models: From Decision Trees to Random Forests