Text Representation | NLP Lecture 4 | Bag of Words | Tf-Idf | N-grams, Bi-grams and Uni-grams

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 ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at [email protected] ✨ 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