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This module introduces regression and linear regression techniques used in Machine Learning. It covers types of linear regression, working principles, advantages, limitations, and practical applications, while also explaining the position of regression within the broader classification of Machine Learning approaches.
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.
