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

The video tutorial discusses the concept of linearity in models, explaining how to identify if a model is linear or nonlinear. It provides a simple definition of linearity using matrices and examines linearity in both model parameters and input variables. The tutorial also highlights the importance of feature transformation, showing how transforming features can help achieve linearity in models that are originally nonlinear.

What is the difference between linearity in parameters and linearity in input variables?

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How can transforming features lead to a linear boundary in a nonlinear original feature space?

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Discuss the implications of a function being linear in transformed inputs.

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