Data Science and Machine Learning (Theory and Projects) A to Z - Introduction to Machine Learning: Machine Learning Mode

The video tutorial discusses the critical decisions a modeler or machine learning expert must make before selecting the best parameters for a model. It emphasizes understanding the loss function, introduced in a previous video, and highlights the importance of making informed choices based on available training data. The tutorial encourages viewers to think about these decisions and the gap between having data and finding optimal parameters.

What types of data are typically available to a modeler when starting a machine learning project?

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Can you describe the gap that exists between having training data and finding the best parameters?

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