Welcome to the ultimate destination for Natural Language Processing enthusiasts - an exhaustive A-Z guide packed with implementations of algorithms, statistical methods, and cutting-edge techniques, all meticulously crafted in Python.
Embark on a journey through the intricate world of NLP as we delve into the realms of sentiment analysis, machine translation, named entity recognition, and much more. Whether you're a seasoned practitioner or just beginning your NLP exploration, our repository is your one-stop-shop for deepening your understanding and honing your skills.
From classic algorithms to state-of-the-art models, we've got you covered with clear, concise implementations that demystify even the most complex concepts. Explore, experiment, and elevate your NLP prowess with our carefully curated collection.
Star this repository if you find it as invaluable to your NLP endeavors as we do. Together, let's push the boundaries of Natural Language Processing and unleash its full potential. ⭐"
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| Topic Name/Tutorial | Video | Code |
|---|---|---|
| 🌐1-What is Natural Language Processing (NLP)⭐️ | 1 | --- |
| 🌐2- Natural Language Processing Tasks and Applications⭐️ | 1 | Content 3 |
| 🌐3- Best Free Resources to Learn NLP-Tutorial⭐️ | Content 5 | Content 6 |
| Topic Name/Tutorial | Video | Code |
|---|---|---|
| 🌐1- Preprocessing_Aassignment_1 | Content 2 |  |
| 🌐2- Supervised ML & Sentiment Analysis⭐️ | 1 | |
| 🌐3-Vocabulary & Feature Extraction⭐️ | 1 | |
| 🌐4-Negative and Positive Frequencies⭐️ | 1 | |
| 🌐5-Text pre-processing⭐️ | 1-2 | |
| 🌐6-Putting it All Together | --- | |
| 🌐7-Logistic Regression Overview | --- | |
| 🌐8-Logistic Regression: Training | --- | |
| 🌐9-Logistic Regression: Testing | --- | |
| 🌐10-Logistic Regression: Cost Function | --- | |
| Lab#1:Visualizing word frequencies | --- | |
| 🌐Lab 2:Visualizing tweets and the Logistic Regression model | --- | |
| 🌐Assignmen:Sentiment analysis with logistic Regression | --- | |
| Topic Name/Tutorial | Video | Code |
|---|---|---|
| 🌐1-Probability and Bayes’ Rule | 1 | |
| 🌐2-Bayes’ Rule | 1 | |
| 🌐3-Naïve Bayes Introduction | 1 | |
| 🌐4-Laplacian Smoothing | 1 | |
| 🌐5-Log Likelihood, Part 1 | 1 | |
| 🌐6-Log Likelihood, Part 2 | 1 | |
| 🌐7-Training Naïve Bayes | 1 | |
| 🌐Lab1-Visualizing Naive Bayes | Content 5 | |
| 🌐Assignment_2_Naive_Bayes | --- | |
| 🌐8-Testing Naïve Bayes | 1 | |
| 🌐9-Applications of Naïve Bayes | 1 | |
| 🌐10-Naïve Bayes Assumptions | 1 | |
| 🌐11-Error Analysis | 1 | |
Week 3 -📚Chapter 3:Vector Space Model
| Topic Name/Tutorial | Video | Code |
|---|---|---|
| 🌐1-Overview | 1 | |
| 🌐2-Autocorrect | 1 | |
| 🌐3-Build Model | 1-2 | |
| 🌐Lecture notebook building_the_vocabulary | --- | |
| 🌐Lecture notebook Candidates from edits | --- | |
| 🌐4-Minimum edit distance | 1 | |
| 🌐5-Minimum edit distance Alogrithem 1 | 1 | |
| 🌐6-Minimum edit distance Alogrithem 2 | 1 | |
| 🌐7-Minimum edit distance Alogrithem 3 | 1 | |
| Topic Name/Tutorial | Video | Code |
|---|---|---|
| 🌐1-N-Grams Overview | 1 | |
| 🌐2-N-grams and Probabilities | 1-2 | |
| 🌐3-Sequence Probabilities | 1 | |
| 🌐3-Understanding the Start and End of Sentences in N-Gram Language Models | 1 | |
| 🌐4-Lecture notebook: Corpus preprocessing for N-grams | --- | |
| 🌐5-Creating and Using N-gram Language Models for Text Prediction and Generation | 1 | |
| 🌐6-How to Evaluate Language Models Using Perplexity: A Step-by-Step Guide⭐️ | 1 | |
| 🌐7-Lecture notebook: Building the language model | --- | |
| 🌐8-Out of Vocabulary Words⭐️ | 1 | |
| 🌐9-Smoothing⭐️ | 1 | |
| Topic Name/Tutorial | Video | Code |
|---|---|---|
| 🌐1-Basic Word Representations⭐️ | 1 | |
| 🌐2-Word Embedding⭐️ | 1-2-3-4 | |
| 🌐3-How to Create Word Embeddings⭐️ | 1 | |
Week - Building Chatbots in Python
💻 Workflow:
-
Fork the repository
-
Clone your forked repository using terminal or gitbash.
-
Make changes to the cloned repository
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Add, Commit and Push
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Then in Github, in your cloned repository find the option to make a pull request
print("Start contributing for Natural Language Processing")
- Make sure you do not copy codes from external sources because that work will not be considered. Plagiarism is strictly not allowed.
- You can only work on issues that have been assigned to you.
- If you want to contribute the algorithm, it's preferrable that you create a new issue before making a PR and link your PR to that issue.
- If you have modified/added code work, make sure the code compiles before submitting.
- Strictly use snake_case (underscore_separated) in your file_name and push it in correct folder.
- Do not update the README.md.
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