Feature scaling is a technique for normalising a set of independent variables or data components. It is also known as data normalisation in data processing and is usually done during the data preprocessing step.
Normalization is useful when your data has variable scales and the technique you're employing, such as k-nearest neighbours and artificial neural networks, doesn't make assumptions about the distribution of your data. The assumption behind standardisation is that your data follows a Gaussian (bell curve) distribution.
Normalization is the process of rescaling values into a range of [0,1]. Typically, standardisation entails rescaling data to a mean of 0 and a standard deviation of 1. (unit variance).
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1.easy to understand all topic
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Akash Sambhaji
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plz provides all notes
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plzz provide notes....
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please provide notes also in pdf
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nice ☺️👍
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please provide course notes
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Shashi Kumar
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great resource to learn data science in hindi. but in this particular video lecture there is a mistake....actually mutually exclusive event can never be independent event.
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Nikhil Fapale
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it really amazing to study....and easily understand difficult concepts...i hope you make more video on like power bi and nueral network model....its really helpful....thank you for these
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