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This module introduces classification algorithms, different types of learners, and major classification approaches used in Machine Learning. It covers model evaluation, the SoftMax function, key performance metrics, and practical use cases for applying classification algorithms to real-world prediction and decision-making 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.
