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This module introduces the K-Nearest Neighbour (KNN) algorithm, its step-by-step procedure, and working mechanism. It covers practical examples, selection of the optimal K value, advantages, disadvantages, real-world applications, and Python implementation for classification and prediction tasks.
This course introduces the fundamentals of Machine Learning, including data types, data preprocessing, regression, classification, clustering, ensemble learning, Support Vector Machines, Decision Trees, K-Nearest Neighbours, Naïve Bayes, Random Forest, and Reinforcement Learning. It equips learners with practical skills to prepare data, build and evaluate Machine Learning models, implement algorithms using Python, and develop effective real-world predictive solutions.
