/NLP_Basics_Tutorial_Examples

NLP_Basics_Tutorial_Examples is a dynamic tutorial folder housing 23 notebooks covering a spectrum of NLP fundamentals, from tokenization and sentiment analysis to advanced topics like transformers and sequence modeling.

Primary LanguageJupyter Notebook

NLP_Basics_Tutorial_Examples

NLP Basics Implementation Tutorial

Welcome to the NLP Basics Implementation Tutorial! This repository contains a series of Jupyter notebook files covering various fundamental aspects of Natural Language Processing (NLP). Whether you're a beginner or looking to deepen your understanding of NLP techniques, these notebooks provide hands-on examples and explanations.

Notebooks Overview

  1. Explore_NLTK.ipynb: Introduction to the Natural Language Toolkit (NLTK) library.

  2. Exploring Word Tokenization and Frequency Analysis with NLTK.ipynb: Implementation of word tokenization using NLTK, alongside frequency distribution analysis. Learn to preprocess text data for further analysis.

  3. Exploring Text Preprocessing Techniques.ipynb: In-depth exploration of various text preprocessing techniques, including stopword removal, lemmatization, and handling special characters.

  4. Reguler_Expression.ipynb: Application of regular expressions for text processing. Understand how to extract and manipulate patterns in text data.

  5. RE_phone_number.ipynb: Validating and extracting phone numbers using regular expressions. A practical example of regex in the context of data validation.

  6. Exploring NLTK's Brown Corpus.ipynb: Categorizing and analyzing the Brown Corpus sections. Understand the diverse range of genres and linguistic data present in the Brown Corpus.

  7. Tokanizers.ipynb: In-depth exploration of various tokenization techniques, including word tokenization, sentence tokenization, and custom tokenization methods.

  8. ELIZA-like Chatbot.ipynb: Implementation of an ELIZA-like chatbot using rule-based techniques. Learn the basics of chatbot design and conversation scripting.

  9. Exploring Collocations_Bigram and Trigram.ipynb: Analyzing collocations, bigrams, and trigrams in text. Understand how certain words tend to co-occur frequently.

  10. Berkeley Restaurant Corpus Bigram Analysis.ipynb: Bigram analysis on the Berkeley Restaurant Corpus. Analyzing linguistic patterns in a specific corpus.

  11. N_gram and Perplexity analysis.ipynb: Understanding N-grams and performing perplexity analysis. Explore the concept of language modeling and evaluate model performance.

  12. Movie Review Sentiment Analysis with Naive Bayes.ipynb: Sentiment analysis using Naive Bayes on movie reviews. Implement a basic sentiment classifier.

  13. Language Detection with Langid.ipynb: Implementing language detection with Langid. Identify the language of a given text snippet.

  14. Lang_translation with Langid.ipynb: Language translation using Langid. Translate text from one language to another using language identification.

  15. Simple text toxicity classi.ipynb: Building a simple text toxicity classifier. Implement a basic model to classify text as toxic or non-toxic.

  16. Skip gram with LR.ipynb: Implementing skip-gram with logistic regression. Understand the basics of word embeddings and skip-gram modeling.

  17. Visualizing Semantic Similarity T-SNE with GLOVE.ipynb: Visualizing semantic similarity using T-SNE with GloVe embeddings. Understand how word embeddings capture semantic relationships.

  18. CBOW for Sentence Embedding with Neural Networks.ipynb: Implementing Continuous Bag of Words (CBOW) for sentence embedding. Learn to represent sentences in vector space.

  19. Next Token Prediction.ipynb: Predicting the next token in a sequence using recurrent neural networks (RNN). Understand the basics of sequence modeling.

  20. Text Summarization.ipynb: Implementing text summarization techniques, including extractive and abstractive summarization.

  21. Exploring nltk.ipynb: Further exploration of the Natural Language Toolkit (NLTK) library, covering advanced functionalities and utilities.

  22. Transformer-Based Machine Translation.ipynb: Implementing machine translation with transformer models. Understand the architecture of transformer models for NLP tasks.

  23. NLP Dive Vectors POS NER RNN Translation.ipynb: Multifaceted exploration of vectors, part-of-speech (POS) and named entity recognition (NER) tagging, and RNN-based translation. Integrates multiple NLP concepts for a comprehensive understanding.

Getting Started

  1. Clone this repository: git clone https://github.com/yourusername/NLP-Basics-Tutorial.git
  2. Open the Jupyter notebooks in your preferred environment.
  3. Run and explore each notebook sequentially for a comprehensive understanding of NLP basics.

Feel free to reach out for any questions or improvements. Happy learning!