Practical Data Science using Python - Linear Regression - Feature Scaling and Cost Functions

The video tutorial covers the importance of feature scaling in data preprocessing, focusing on normalization and standardization techniques. It explains how different scales in data can affect machine learning models, particularly those using gradient descent. The tutorial also discusses data splitting into training and test sets, model training, and the importance of generalization. Finally, it highlights the assumptions of linear regression, emphasizing the need for a linear relationship in the data.

Why is it important to split data into training and test sets?

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What is the assumption of linearity in the context of linear regression?

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How does the presence of a linear relationship in data affect the performance of a linear regression model?

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What challenges arise when there is no linear relationship in the data for linear regression?

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