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This module introduces the fundamentals of Machine Learning, including its meaning, functioning, key features, and importance. It covers major classifications of Machine Learning, its historical development, and the present-day role of Machine Learning in solving real-world problems through data-driven prediction and automated decision-making.
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.
