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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📕Course 01 -Classification and Vector Spaces

Week 0-📚Chapter 1:Introduction

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

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

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	---

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

• Fork the repository

• Clone your forked repository using terminal or gitbash.

• Make changes to the cloned repository

• Add, Commit and Push

• 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.

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

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!🚀