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This module introduces the Support Vector Machine (SVM) algorithm, its key terms, objectives, and hyperplanes as decision surfaces. It covers linear and non-linear classification, types of SVM, advantages, disadvantages, and real-world applications for effective classification and predictive modeling.
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.
