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This module introduces the fundamentals of Machine Learning, including core concepts, learning types, data preparation, model training, prediction, evaluation, and accuracy measurement. Learners gain a foundational understanding of how machine learning models are developed and assessed for real-world applications.
This course introduces the complete data science workflow, covering dataset understanding, data cleaning and preprocessing, exploratory data analysis, basic statistics, data visualization, Python libraries (NumPy and Pandas), and machine learning fundamentals. Learners gain hands-on experience in analyzing, transforming, visualizing, and interpreting data, apply data science techniques to real-world case studies, and complete an end-to-end project with implementation, presentation, and reporting of results.
