Feature Engineering for better classification.

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A very short duration video demonstrating through a visualization that how projecting data into a higher dimensional space makes it easy for #classificationalgorithms to properly classify the data and makes the projected data linearly separable. After watching this video, one will be able to easily understand the concept and answer several of the following questions:

1. How #featureengineering works ?
2. How to make a #nonlinearlyseparable data as #linearlyseparable ?
3. How #featureengineering makes data #linearlyseparable ?
4. What happens when we perform #featureengineering on the data ?
5. what is meant by projecting data into a #higherdimensional space ?
6. What happens when data flows from previous layer of a #neuralnetwork to next layer in #forwardpass ?
7. How #kerneltrick in SVM works ?
8. What kernel trick actually does in SVM ?
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Full video can be seen here:

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For detailed post/notes related to this video, navigate to the following links:

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