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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- 🎯 Why Join This Course?
- 💡 How to Participate?
- 🌍 Join Our Community
- 📬 Stay Updated with Weekly NLP Lessons!
- 🚀 Let's Build NLP Together!
- 📌 Course Modules & Resources
- 📕 NLP Resources (Courses, Books, Tools)
- 💻 Workflow
- ⚙️ Things to Note
- 🔍 Explore more
- ✨ Top Contributors
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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. Convert this for computer vision so that it attract contributor
🚀 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!
Please enrolled in the following courses to strengthen knowledge and practical skills in Natural Language Processing (NLP). These courses are designed to provide both theoretical understanding and hands-on experience with real-world NLP applications.
🔗 Basic Natural Language Processingl
1- Covers foundational concepts such as tokenization, POS tagging, lemmatization, and basic text classification.
1- Focuses on probabilistic techniques including n-gram models, Naive Bayes, and Hidden Markov Models.
- Explores advanced topics such as RNNs, LSTMs, GRUs, and their application in language modeling and machine translation.
💡 These courses are part of a structured NLP curriculum offered by Coursesteach, designed by Couresteach team, and emphasize practical implementation using Python and deep learning libraries.
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Join us in creating, sharing, and implementing NLP solutions. Your contributions will help advance open-source AI education globally. 💡🤖
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| Topic Name/Tutorial | Video | 💻 Colab Implementation |
|---|---|---|
| ✅1-What is Natural Language Processing (NLP)-g⭐️-Substack Link | 1 | --- |
| ✅2- Natural Language Processing Tasks and Applications-g⭐️ | 1 | Content 3 |
| ✅3- Best Free Resources to Learn NLP-Tutorial-g | Content 5 | Content 6 |
- Understand the difference between supervised and unsupervised learning.
- Learn how sentiment classification works using labeled datasets.
| Topic Name/Tutorial | Video | 💻 Colab Implementation |
|---|---|---|
| ✅1- Preprocessing_Aassignment_1 | Content 2 |  |
| ✅2- Supervised ML & Sentiment Analysis-g | Video 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 | 1 | |
| ✅7-Logistic Regression Overview-S | 1 | |
| ✅8-Logistic Regression: Training-s | 1 | |
| ✅9-Logistic Regression: Testing⭐️ | 1 | |
| ✅10-Logistic Regression: Cost Function⭐️ | 1 | |
| ✅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 | |
📌 Learning Objectives or Outcomes
1- Understand the Fundamentals of Word Embeddings
2- Master the CBOW Model
3- Evaluate Word Embeddings Effectively
4- Apply Practical Skills in Word Embedding Tasks
| Topic Name/Tutorial | Video | Code | Resources |
|---|---|---|---|
| 🌐1-Basic Word Representations⭐️ | 1 | |
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| 🌐2-Word Embedding⭐️ | 1-2-3-4 | |
1 |
| 🌐3-How to Create Word Embeddings⭐️ | 1 | |
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| 🌐4-Word Embedding Methods⭐️ | 1 | |
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| 🌐5-Continuous Bag-of-Words Model⭐️ | 1-2 | |
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| 🌐6-Cleaning and Tokenization⭐️ | 1 | |
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| 🌐7-Sliding Window⭐️ | 1 | |
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| 🌐8-Transforming Words into Vectors⭐️ | 1 | |
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| 🌐9-Lecture Notebook - Data Preparation⭐️ | --- | |
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| 🌐9-Architecture of the CBOW Model⭐️ | 1 | |
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| 🌐10-Architecture of the CBOW Model-Dimensions⭐️ | 1 | |
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| 🌐11-Architecture of the CBOW Model-Dimensions 2⭐️ | 1 | |
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| 🌐12-Architecture of the CBOW Model-Activation Functions⭐️ | 1 | |
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| 🌐Lecture Notebook - Intro to CBOW model⭐️ | --- | |
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| 🌐13-Training a CBOW Model-Cost Function⭐️ | 1 | |
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| 🌐14-Training a CBOW Model-Forward Propagation⭐️ | 1 | |
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| 🌐15-Training a CBOW Model-Backpropagation and Gradient Descent⭐️ | 1 | |
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| 🌐16-Lecture Notebook - Training the CBOW model⭐️ | --- | |
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| 🌐17-Extracting Word Embedding Vectors⭐️ | 1 | |
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| 🌐Lecture Notebook - Word Embeddings⭐️ | --- | |
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| 🌐18-Evaluating Word Embeddings-Intrinsic Evaluation⭐️ | 1 | |
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| 🌐19-Evaluating Word Embeddings-Extrinsic Evaluation⭐️ | 1 | |
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| 🌐Lecture notebook: Word embeddings step by step⭐️ | --- | |
🎯 Course Description
This course dives deep into sequence modeling techniques for Natural Language Processing (NLP), covering foundational to state-of-the-art architectures like RNNs, GRUs, LSTMs, and Transformer models. Learners will explore language modeling, machine translation, text summarization, named entity recognition, and more. The course emphasizes both theoretical understanding and practical implementation through coding assignments, mini-projects, and real-world datasets.
| Topic Name/Tutorial | Video | Code |
|---|---|---|
| 🌐1-Course 3 Introduction | 1 | |
| 🌐2-Neural Networks for Sentiment Analysis | 1 | |
| 🌐3-Dense Layers and ReLU | 1 | |
| 🌐4-Embedding and Mean Layers | 1 | |
| 🌐5-Traditional Language models | 1 | |
| 🌐6-Recurrent Neural Networks | 1 | |
| 🌐7-Application of RNN | 1 | |
| 🌐9-Math in Simple RNNs | 1 | |
| Topic Name/Tutorial | Video | Code |
|---|---|---|
| 🌐1-Overview | 1 | |
Week - Building Chatbots in Python
| Title/link | Description | Reading Status | Knlowdgef Level | FeedBack |
|---|---|---|---|---|
| ✅ 1-Natural Language Processing Specialization | by Eddy Shyu,Cousera,Goog | InProgress | Beginer | Good |
| ✅ 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 | ||
| ✅ 4-A Code-First Intro to Natural Language Processing | It is free course and it contain notes and video,Andrej Karpathy | Pending | ||
| ✅ 5-AI for Medicine Specialization | It is free course and it contain notes and video,Andrej Karpathy | Pending | ||
| ✅ 6-Fundamentals of AI Agents Using RAG and LangChain by IBM | Learn retrieval-augmented generation (RAG) applications and processes. | Pending | ||
| ✅ 7-Large Language Model Agents | Covers fundamental LLM agent concepts and required abilities. | Pending | ||
| ✅ 8-AI Agentic Design Patterns with AutoGen | Learn to make and customize multi-agent systems using AutoGen.. | Pending | ||
| ✅ 9-AI Agents in LangGraph by deeplearning.ai | Build an agent from scratch, then rebuild it using LangGraph.by Harrison Chase, Rotem Weiss | Pending | ||
| ✅ 10-Serverless Agentic Workflows with Amazon Bedrockby deeplearning.ai | Build and deploy serverless agentic applications.by Mike Chambers | Pending | ||
| ✅ 11-Multi-AI Agent Systems with CrewAI deeplearning.ai | Learn principles of designing effective AI agents and organizing agent teams..by João Moura | Pending | ||
| ✅ 12-Smol Agents: Build & Deploy by Hugging Face | Study AI agents in theory, design, and practical application | Pending | ||
| ✅ 13-Advanced Large Language Model Agents by | Learn advanced topics like complex reasoning and planning for LLM agents. by Xinyun Chen | 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 |
| ✅ 7-NLP with Python | it implement nlp concept in python | Pending |
| ✅ 8-nlp-notebooks | it implement nlp concept in python | Pending |
| ✅ 9-CS 4650 and 7650 | This course gives an overview of modern data-driven techniques for natural language processing. | Pending |
| ✅ 10-LLM course | This course gives an overview of modern data-driven techniques for natural language processing. | Pending |
| ✅ 11-Awesome-LLM | It also contains frameworks for LLM training, tools to deploy LLM, courses and tutorials about LLM. | Pending |
| ✅ 11-LLM-Agent-Paper-List | This repository is a treasure trove of research papers on LLM-based agents.. | Pending |
| ✅ 12-Masterclass: Large Language Models for Data Science | This repository focuses on integrating LLMs into workflows. It provides an ebook-style introduction to various topics such as prompt engineering, local LLMs, retrieval-augmented generation (RAG) problems, and more | Pending |
| ✅ 13-Awesome LLM Apps | A curated collection of awesome LLM apps built with RAG and AI agents. This repository features LLM apps that use models from OpenAI, Anthropic, Google, and open-source models like DeepSeek, Qwen or Llama that you can run locally on your computer. | Pending |
| ✅ 14-Hands-On Large Language Models | Welcome! In this repository you will find the code for all examples throughout the book Hands-On Large Language Models written by Jay Alammar and Maarten Grootendorst which we playfully dubbed: | Pending |
| ✅ 15-Awesome-Multimodal-Large-Language-Models | The first comprehensive survey for Multimodal Large Language Models (MLLMs). ✨ | Pending |
| ✅ 16-Build a Large Language Model (From Scratch) | This repository contains the code for developing, pretraining, and finetuning a GPT-like LLM and is the official code repository for the book Build a Large Language Model (From Scratch). | Pending |
| ✅ 17-AI-Notebooks by Marktechpost | AI-Tutorials/Implementations and Notebooks. | Pending |
| Title/Link | Description |
|---|---|
| Theresanaiforthat | Directory of AI tools for every possible use case. |
| ChatGPT | Chatbot powered by OpenAI for general and professional use. |
| Copilot | Microsoft's AI assistant integrated across their ecosystem. |
| Poe | Multi-AI platform enabling access to various models. |
| Groq | High-performance inference for LLMs. |
| Hugging Face | Hub for AI models, datasets, and ML tools. |
| Mistral Chat | Chatbot powered by Mistral models. |
| Pi (Inflection AI) | Personalized AI chatbot assistant. |
| DeepSeek Chat | Open-source chat assistant by DeepSeek. |
| Andi Search | AI-powered search engine with conversational answers. |
💻 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!🚀