Word Embeddings: Theoretical Foundation | Codebasics

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Contents

1.1: What is NLP?
1.2: Three Techniques of doing NLP
1.3: NLP Tasks
1.4: NLP Pipeline
1.5: Tools Overview
1.6: Quiz
1.7: Chapter Summary

2.1: Tokenization Techniques
2.2: Tokenization in Spacy
2.3: Language Processing Pipeline in Spacy
2.4: Stemming & Lemmatization
2.5: Part of Speech (POS) Tagging
2.6: Stop Words
2.7: Named Entity Recognition (NER)
2.8: Regular Expressions (Regex)
2.9: Quiz
2.10: Exercise
2.11: Chapter Summary

3.1: Introduction to Text Representation
3.2: Label and One Hot Encoding
3.3: Bag of Words (BOW)
3.4: Bag of n-grams
3.5: TF-IDF
3.6: Word Embeddings: Theoretical Foundation
3.7: Word Embeddings in Spacy
3.8: News Classification using Spacy Word Embeddings
3.9: Quiz
3.10: Exercise
3.11: Chapter Summary

4.1: Introduction to Hugging Face
4.2: Pipelines
4.3: BERT, DistilBERT, RoBERTa, ALBERT
4.4: Tokenizers
4.5: Model Fine Tuning
4.6: Quiz
4.7: Exercise
4.8: Chapter Summary

5.1: Final Quiz

6.1: Transition to Gen AI

Instructor

Dhaval Patel

Dhaval Patel

Data Entrepreneur | YouTuber | Ex - Bloomberg, Nvidia

I have 17 years of experience in Programming and Data Science working for big tech companies like NVIDIA and Bloomberg. I also run a famous YouTube channel called Codebasics where I pursue my passion for teaching. Codebasics is one of the top channels on YouTube when it comes to data science, machine learning, data structures, etc. I firmly believe that “Anyone can code” and I use analogies, simple explanations, and step-by-step storytelling to explain difficult concepts in such a way that even a high school student can understand them easily.

Word Embeddings: Theoretical Foundation

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