Natural Language Processing Tutorials in Python. Part-2. Vectorization. BagofWords, TF-IDF, Word2Vec

First of all Sorry my webcam was not working so no face this time. Will be in the next video for Sure. :)
Now enjoy...

🔥 Welcome to our Natural Language Processing (NLP) tutorial in Malayalam! 🔥

Are you curious about how computers understand human language? Look no further! In this NLP tutorial, we will dive into the fascinating world of NLP and demonstrate live coding in Python, covering essential concepts like Bag of Words, TF-IDF, and Word2Vec. Whether you're a data science enthusiast or a Malayalam Python coder, this tutorial is for you!

⭐️ What's in this NLP Tutorial? ⭐️

🎯 Bag of Words (BoW) Explained:
Learn how to represent text data as numerical vectors using the Bag of Words model. This simple but powerful technique is the foundation of many NLP applications like sentiment analysis, text classification, and more!

📈 TF-IDF (Term Frequency-Inverse Document Frequency):
Discover how to measure the importance of words in a document relative to a corpus of documents. TF-IDF is crucial for information retrieval and text mining tasks.

🌐 Word2Vec:
Uncover the magic of word embeddings with Word2Vec! Witness how words are transformed into dense vectors, capturing semantic relationships and enabling machine learning on text data.

📝 Understanding the Theme of NLP in Simple Terms:
We'll break down the core theme of Natural Language Processing in straightforward language. You'll grasp the primary goals, challenges, and applications of NLP in the real world.

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🚀 Let's embark on this exciting journey into the world of Natural Language Processing together! Hit that subscribe button and let's dive right in! 🚀

start 0:00
introduction 01:00
Bag of Words 01:55
WordCloud 18:16
BOW Pros and Cons 19:41
TF-IDF 21:27
Why TF-IDF 23:40
Why TF-ID shines over BOW 28:00
TF-IDF Pros and Cons 29:32

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NLP Tutorial
Python NLP
NLP Python Tutorial
Bag of Words
TF-IDF
Word2Vec
Text Analysis
Sentiment Analysis
Text Classification