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This module introduces data preprocessing techniques for identifying and handling missing values, duplicate records, inconsistent data, noise and outliers, ensuring datasets are clean, accurate and suitable for Machine Learning model development
This course introduces the fundamentals of Artificial Intelligence and Machine Learning, covering AI concepts and applications, types of machine learning, Python programming, essential libraries, dataset handling, data preprocessing, feature engineering, supervised and unsupervised learning, dimensionality reduction, model evaluation and deep learning. It equips learners with practical knowledge and essential skills to analyse data, develop machine learning models and apply AI-based solutions to real-world problems.
