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This module introduces the Decision Tree classification algorithm, its meaning, purpose, and working process. It covers attribute selection measures such as information gain, entropy, and Gini index, along with advantages, disadvantages, and Python implementation for solving 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.
