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This module introduces K-Means clustering as an unsupervised Machine Learning technique. It covers the working process of the algorithm, selection of the optimal K value, the Elbow Method, advantages, limitations, and real-world applications for data grouping, segmentation, and pattern discovery.
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.
