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This module introduces logistic regression and its importance in classification problems. It covers differences between linear and logistic regression, the sigmoid function, mathematical modeling, visualization, applications, advantages, limitations, evaluation metrics, and practical examples for predicting categorical outcomes
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.
