Welcome to Collaborative Natural Language Processing (NLP) Course👋🛒

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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📚 Table of Contents

• 🎯 Why Join This Course?
• 💡 How to Participate?
• 🌍 Join Our Community
• 📬 Stay Updated with Weekly NLP Lessons!
• 🚀 Let's Build NLP Together!
• 📌 Course Modules & Resources
  • 📕 Course 01 – Classification and Vector Spaces
  • 📕 Course 02 – NLP with Probabilistic Models
  • 📕 Course 03 – NLP with Sequence Models
  • 📕 Course 04 – NLP with Attention Models
  • 📕 Course 05 – Building Chatbots in Python
• 📕 NLP Resources (Courses, Books, Tools)
• 💻 Workflow
• ⚙️ Things to Note
• 🔍 Explore more
• ✨ Top Contributors

🎯 Why Join This Course?

1. 📖 Comprehensive Learning: Covers all major NLP topics, from basics to cutting-edge deep learning techniques.

2. 🛠 Practical Implementation: Each topic includes hands-on coding exercises, Jupyter notebooks, and real-world projects.

3. 🤝 Collaborative Learning: ork with students and researchers worldwide through GitHub discussions, issue tracking, and dedicated forums..

4. 🔥 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

💡 How to Participate?

🚀 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!

🎓 Enrolled Courses

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.

🔗 NLP Probabilistic Models

1- Focuses on probabilistic techniques including n-gram models, Naive Bayes, and Hidden Markov Models.

🔗 NLP with Sequence Model

1. 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.

🌍 Join Our Community

🔗 YouTube Channel

🔗 SubStack Blogs

🔗 Facebook

🔗 LinkedIn

📬 Need Help? Connect with us on WhatsApp

🚀 Let's Build NLP Together!

Join us in creating, sharing, and implementing NLP solutions. Your contributions will help advance open-source AI education globally. 💡🤖

🔗 Start Learning NLP Now!

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📬 Stay Updated with Weekly NLP Lessons!

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📌 Course Modules & Resources

📕Course 01 -Classification and Vector Spaces

🔹Week 0-Chapter 1:Introduction

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

🔹Week 1-Chapter 2:Sentiment Analysis (logistic Regression)

📌 Learning Objectives or Outcomes

• 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	---

Week 2-📚Chapter3:Sentiment Analysis using Naive Bayes

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-Vector Space Models	1
🌐2-Word by Word and Word by Doc	1
🌐3-Euclidean Distance	1-2
🌐4-Cosine Similarity: Intuition	1-2
🌐5-Cosine Similarity	1
🌐6-Manipulating Words in Vector Spaces	1
🌐7-Visualization and PCA	1
🌐8-Lab1_Linear_algebra_in_Python_with_Numpy.ipynb
🌐8-PCA Algorithm	1-2
🌐9-Lab:2_Manipulating word embeddings

Week 4 - 📚Chapter 4:Machine Translation and Document Search

Topic Name/Tutorial	Video	Code
🌐1-Transforming word vectors	1
🌐2-Lab1 Rotation matrices R2	--
🌐3-K-nearest neighbors	1
🌐4-Hash tables and hash functions	1
🌐5-Locality sensitive hashing	1
🌐6-Multiple Planes-r	1
🌐7-Approximate nearest neighbors	1
🌐7-Lab2:Hash tables	1
🌐8-Searching documents	1

📕Course 02 -Natural Language Processing with Probabilistic Models

Week 1-📚Chapter1:Autocorrect and Mininum Edit Distance

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

Week 2-📚Chapter2:Part of Speech Tagging and Hidden Markov Models

Topic Name/Tutorial	Video	Code
🌐1-Part of Speech Tagging	1-2
🌐2-Markov Chains	1
🌐3-Markov Chains and POS Tags	1
🌐4-Hidden Markov Models	1
🌐5-Calculating Probabilities	1-2
🌐6-Populating the Emission Matrix	1
🌐Lecture Notebook - Working with tags and Numpy	--
🌐7-The Viterbi Algorithm	1-2
🌐8-Viterbi: Initialization,Forward Pass,Backward Pass	1-2-3
🌐9-Lecture Notebook - Working with text file	--
🌐10-Assignment: Part of Speech Tagging	--

Week 3-📚Chapter3:Autocomplete and Language models

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

Week 3-📚Chapter4:Word embedding with neural network

📌 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
🌐2-Word Embedding⭐️	1-2-3-4		1
🌐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
🌐15-Training a CBOW Model-Backpropagation and Gradient Descent⭐️	1
🌐16-Lecture Notebook - Training the CBOW model⭐️	---
🌐17-Extracting Word Embedding Vectors⭐️	1
🌐Lecture Notebook - Word Embeddings⭐️	---
🌐18-Evaluating Word Embeddings-Intrinsic Evaluation⭐️	1
🌐19-Evaluating Word Embeddings-Extrinsic Evaluation⭐️	1
🌐Lecture notebook: Word embeddings step by step⭐️	---

📕Course 03 -Natural Language Processing with Sequence Models

🎯 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.

Week 1-📚Chapter1:Recurrent Neural Networks for Language modeling

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
🌐10-Cost Function for RNNs	1

📕Course 04 -Natural Language Processing with Attention Models

Week 1-📚Chapter1:Autocorrect and Mininum Edit Distance

Topic Name/Tutorial	Video	Code
🌐1-Overview	1

📕Course 05 -Building Chatbots in Python

Week - Building Chatbots in Python

• Building Chatbots in Python

📕 Natural-Language Processing Resources

👁️ Chapter1: - Free Courses

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

👁️ Chapter2: - Important Website

Title/link	Description	Code
✅1- learngood	It is Videos and github	---

👁️ Chapter3: - Important Social medica Groups

Title/link	Description	Code
🌐1- Computer Science courses with video lectures	It is Videos and github	---

👁️ Chapter4: - Free Books

Title/link	Description	Code
🌐1- Computer Science courses with video lectures	It is Videos and github	---

👁️ Chapter5: - Github Repository

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

👁️ Chapter1: - 🔍 General Tools and Chatbots

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:

• 🔹 Fork the repository and submit Pull Requests (PRs) for changes.

• 🔹Clone your forked repository using terminal or gitbash.

• 🔹Make changes to the cloned repository

• 🔹Add, Commit and Push

• 🔹 Reviewers will approve or request changes before merging.

• 🔹Then in Github, in your cloned repository find the option to make a pull request

• 🔹 Nobody can push directly to main (unless explicitly allowed in settings).

🔹print("Start contributing for Natural Language Processing")

⚙️ Things to Note

• 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 more

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!”

• Natural Language Processing with Probabilistic models Course
• Natural Language Processing course

✨Top Contributors

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!🚀