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This module introduces ensemble learning, its working principles, and challenges in developing ensemble models. It covers the Random Forest classifier, its assumptions, features, need, and functioning, along with practical applications, advantages, disadvantages, and implementation 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.
