Welcome to the Natural Language Processing (NLP) Course, an open-source initiative to learn, implement, and master NLP concepts using Python. Whether you're a student, researcher, or AI enthusiast, this repository provides a structured, hands-on approach to mastering NLP from fundamentals to advanced topics.
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📖 Comprehensive Learning: Covers all major NLP topics, from basics to cutting-edge deep learning techniques.
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🛠 Practical Implementation: Each topic includes hands-on coding exercises, Jupyter notebooks, and real-world projects.
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🤝 Collaborative Learning: ork with students and researchers worldwide through GitHub discussions, issue tracking, and dedicated forums..
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🔥 AI-Powered Course: Stay ahead with industry-relevant techniques like transformers, BERT, GPT, and more.
🚀 Fork & Star this repository
👩💻 Explore and Learn from structured lessons
🔧 Enhance the current blog or code, or write a blog on a new topic
🔧 Implement & Experiment with provided code
🤝 Collaborate with fellow NLP enthusiasts
📌 Contribute your own implementations & projects
📌 Share valuable blogs, videos, courses, GitHub repositories, and research websites
💡 Start your NLP journey today!
📬 Need Help? Connect with us on WhatsApp
Join us in creating, sharing, and implementing NLP solutions. Your contributions will help advance open-source AI education globally. 💡🤖
🔗 Start Learning NLP Now!
| Topic Name/Tutorial | Video | 💻 Colab Implementation |
|---|---|---|
| ✅1-What is Natural Language Processing (NLP)⭐️-Substack Link | 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 | 💻 Colab Implementation |
|---|---|---|
| ✅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-s | 1-2 | |
| ✅6-Putting it All Together-S | --- | |
| ✅7-Logistic Regression Overview-S | --- | |
| ✅8-Logistic Regression: Training-s | --- | |
| 🌐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 | |
| 🌐4-Word Embedding Methods⭐️ | 1 | |
| 🌐5-Continuous Bag-of-Words Model⭐️ | 1-2 | |
| 🌐6-Cleaning and Tokenization⭐️ | 1 | |
| 🌐7-Sliding Window⭐️ | 1 | |
| 🌐8-Transforming Words into Vectors⭐️ | 1 | |
| 🌐9-Lecture Notebook - Data Preparation⭐️ | --- | |
| 🌐9-Architecture of the CBOW Model⭐️ | 1 | |
| 🌐10-Architecture of the CBOW Model-Dimensions⭐️ | 1 | |
| 🌐11-Architecture of the CBOW Model-Dimensions 2⭐️ | 1 | |
| 🌐12-Architecture of the CBOW Model-Activation Functions⭐️ | 1 | |
| 🌐Lecture Notebook - Intro to CBOW model⭐️ | --- | |
| 🌐13-Training a CBOW Model-Cost Function⭐️ | 1 | |
| 🌐14-Training a CBOW Model-Forward Propagation⭐️ | 1 | |
| Topic Name/Tutorial | Video | Code |
|---|---|---|
| 🌐1-Course 3 Introduction | 1 | |
| Topic Name/Tutorial | Video | Code |
|---|---|---|
| 🌐1-Overview | 1 | |
Week - Building Chatbots in Python
| Title/link | Description | Reading Status |
|---|---|---|
| ✅ 1-Natural Language Processing Specialization | by Eddy Shyu,Cousera,Goog | InProgress |
| ✅ 2-Applied Language Technology | It is free course and it contain notes and video | Pending |
| ✅ 3-Large Language Models for the General Audience | It is free course and it contain notes and video,Andrej Karpathy | Pending |
| Title/link | Description | Code |
|---|---|---|
| ✅1- learngood | It is Videos and github | --- |
| Title/link | Description | Code |
|---|---|---|
| 🌐1- Computer Science courses with video lectures | It is Videos and github | --- |
| Title/link | Description | Code |
|---|---|---|
| 🌐1- Computer Science courses with video lectures | It is Videos and github | --- |
| Title/link | Description | Status |
|---|---|---|
| ✅ 1- Computer Science courses with video lectures | It is Videos and github | Pending |
| ✅ 2- ML YouTube Courses | Github repisotry contain couress | Pending |
| ✅ 3- ml-roadmap | Github repisotry contain couress | Pending |
| ✅ 4-courses & resources | It is course of all AI domain | Pending |
| ✅ 5-GenAI Agents: Comprehensive Repository for Development and Implementation | collections of Generative AI (GenAI) agent tutorials and implementations | Pending |
| ✅ 6-nlp-notebooks | it implement nlp concept , it is by nlptown | Pending |
| Title/link | Description | Code |
|---|---|---|
| 🌐1- Computer Science courses with video lectures | It is Videos and github | --- |
💻 Workflow:
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🔹 Fork the repository and submit Pull Requests (PRs) for changes.
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🔹Clone your forked repository using terminal or gitbash.
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🔹Make changes to the cloned repository
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🔹Add, Commit and Push
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🔹 Reviewers will approve or request changes before merging.
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🔹Then in Github, in your cloned repository find the option to make a pull request
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🔹 Nobody can push directly to main (unless explicitly allowed in settings).
🔹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.
Explore cutting-edge tools and Python libraries, access insightful slides and source code, and tap into a wealth of free online courses from top universities and organizations. Connect with like-minded individuals on Reddit, Facebook, and beyond, and stay updated with our YouTube channel and GitHub repository. Don’t wait — enroll now and unleash your NLP potential!”
We would love your help in making this repository even better! If you know of an amazing NLP course that isn't listed here, or if you have any suggestions for improvement in any course content, feel free to open an issue or submit a course contribution request.
Together, let's make this the best AI learning hub website! 🚀
Thanks goes to these Wonderful People. Contributions of any kind are welcome!🚀