Text Representation | NLP Lecture 4 | Bag of Words | Tf-Idf | N-grams, Bi-grams and Uni-grams
Channel: CampusX
Date: 2021-12-16

In natural language processing, text representation plays a vital role in capturing the meaning and structure of textual data. This video explores three fundamental text representation techniques: Bag of Words, Tf-Idf (Term Frequency-Inverse Document Frequency), and N-grams (Uni-grams and Bi-grams). Each method has its unique approach to encoding and extracting information from text, making it essential for data scientists and NLP enthusiasts to grasp these concepts.

Assignment - https://colab.research.google.com/drive/1T9HAtxKs9LS7xXHb0OmFNWbDOf1an6RG?usp=sharing

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✨ Hashtags✨
#TextRepresentation #BagOfWords #TfIdf #NGrams #NLP #DataScience #machinelearning 

⌚Time Stamps⌚

00:00 - Intro
01:10 - Plan of Attack
02:56 - Introduction
03:25 - What is feature extraction from text?
04:49 - Why do we need feature extraction?
07:30 - Why is this difficult to do?
11:00 - What is the core idea behind this?
12:12 - What are the Techniques?
14:24 - Common Terms
18:00 - One Hot Encoding
33:25 - Bag of Words
57:45 - N-grams/Bi-grams/Tri-grams
01:13:45 - Benefits of N Grams
01:14:25 - Disadvantages N Grams
01:16:34 - Tf-Idf
01:38:46 - Custom Features
01:41:45 - Assignment