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The video tutorial discusses feature scaling and normalization, emphasizing the importance of preprocessing raw data. Feature scaling involves adjusting the scale of features to improve their distribution. An example is dividing every value of a continuous feature by 2, which shrinks the histogram. Different techniques like subtracting and dividing by numbers can also be used for feature scaling. The tutorial aims to clarify any misconceptions about these concepts.