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This module introduces different types of data used in Machine Learning, including numerical, categorical, time-series, textual, and image data. It covers the fundamentals and importance of data preprocessing, importing datasets, and feature scaling techniques to prepare data for effective Machine Learning model development.
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.
