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The video tutorial discusses feature scaling in machine learning models. It explains the difference between normalization and standardization. Feature scaling is important to ensure that all features in the data have the same scale. A practical example is given with a data set containing interest rates, employment score, and S&P 500. The tutorial highlights the need for scaling due to the different ranges of values in the data columns. Minimum and maximum values for interest rates are provided as an example.