Your Starting Point in Natural Language Processing (NLP): Tokenization, Bag of Words & Embeddings

In this video, we’ll break down one of the most fundamental concepts in Natural Language Processing (NLP) — turning raw text into meaningful numerical representations.

We’ll start with tokenization, where text is split into smaller pieces called tokens. Then, we’ll move to Bag of Words, a simple yet powerful way of representing text based on word frequency.
Finally, we’ll explore word embeddings, a more advanced and intelligent method that captures the meaning and relationships between words.

This video uses visual explanations and real examples to make these concepts clear and intuitive. You’ll also learn how these steps are implemented in Python, both manually and using spaCy, a popular NLP library. We’ll talk about stop words, punctuation, and normalization.
Whether you’re a student, data scientist, or just starting your NLP journey, this tutorial will give you a solid foundation for understanding how machines read language.
What you’ll learn:
1.What tokenization is and why it matters. 2.How Bag of Words works with a spam detection example. 3.The intuition behind word embeddings. 4.How similar words are placed close together in vector space. 5.Why stop words are often removed in classical NLP but not always in modern models. 6.How to use spaCy to simplify the entire pipeline. #NLP #Tokenization #Embeddings #Python #spaCy #MachineLearning #DataScience #TextProcessing #AIBasics #NaturalLanguageProcessing #CodingTutorial #AIForBeginners #TextAnalysis