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This module introduces Reinforcement Learning algorithms, their basic framework, key components, core pillars, and workflow. It covers the mathematical foundations of Reinforcement Learning, exploration versus exploitation, evolution of RL capabilities, advantages, limitations, industrial applications, and comparison with other Machine Learning paradigms.
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.
