Data Science and Machine Learning (Theory and Projects) A to Z - Machine Learning Models and Optimization: Optimization

The video tutorial discusses error minimization in machine learning, focusing on finding parameter values that minimize total error. It explains the concept of optimization, handling positive and negative errors, and introduces mean squared error (MSE) as a common method for error measurement. The tutorial also covers hyperparameters, model selection, and the overall flow of training, including the role of optimization algorithms in finding the best parameters. The video concludes with a preview of hands-on experience with linear regression in the next session.

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