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Explore the fundamental concepts of how machines learn from data to make intelligent decisions.
Understand the mechanics of training models using labeled data for classification and prediction tasks.
Learn how algorithms discover hidden patterns and structures in unlabeled datasets.
Dive into predictive modeling using Linear and Polynomial regression techniques.
Master the math behind measuring prediction errors, including MSE, MAE, and cost function optimization.
Learn how to properly assess regression models using R² and Adjusted R² scoring.
Identify the causes and solutions for when a model fails to capture the underlying pattern in data.
Recognize when a model learns noise instead of signal, and how to prevent it from performing poorly on new data.