Featurization of Text data

It first constructs a dictionary of the set of all the words in the TEXT. It consists of all unique words in the TEXT. It represents word as a sparse matrix.

1 — Bag of Words

It first constructs a dictionary of the set of all the words in the TEXT. It consists of all unique words in the TEXT. It represents word as a sparse matrix.

For each document(row), find unique words where each word is a different dimension. Each cell consists of the number of times the word occurs in the respective row.

d will be very large where most of the cells have zero value. This is the reason a sparse matrix will be formed.

If two vectors are very similar then they will be very closer.

So length between two vectors is d=|(Term1-Term2)| norm equal to square root of d.

Code:

Drawback:

BOW does not take semantic meaning into consideration. Ex. tasty and delicious have the same meaning but BOW considers as separate.

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Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.

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