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This module explores major applications of Machine Learning, including image and speech recognition, traffic prediction, product recommendation, self-driving cars, stock market prediction, and healthcare. It also covers the complete Machine Learning life cycle, including data collection, data preparation, data wrangling, data analysis, model training and testing, and deployment.
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.
