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This module introduces kernel tricks in Support Vector Machines and explains their need for handling non-linear data. It covers the working mechanism of kernel functions, key parameters such as gamma, major types of kernels, and practical implementation for solving complex classification problems.
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.
