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Learn the fundamental concepts of machine learning through this comprehensive Tamil-language lecture that covers the basic principles, methodologies, and applications of ML systems. Explore key topics including supervised and unsupervised learning paradigms, algorithm selection criteria, data preprocessing techniques, and model evaluation methods. Understand the mathematical foundations underlying popular machine learning algorithms such as linear regression, decision trees, and clustering techniques. Discover real-world applications of machine learning across various domains including healthcare, finance, and technology. Gain insights into the workflow of machine learning projects from data collection and feature engineering to model training and deployment. Examine common challenges in machine learning such as overfitting, underfitting, and bias-variance tradeoff along with strategies to address them. Develop an understanding of when and how to apply different machine learning approaches based on problem requirements and data characteristics.
