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https://github.com/youssefhosni/awesome-ai-data-github-repos
A collection of the most important Github repos for ML, AI & Data science practitioners
https://github.com/youssefhosni/awesome-ai-data-github-repos
List: awesome-ai-data-github-repos
Last synced: about 1 month ago
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A collection of the most important Github repos for ML, AI & Data science practitioners
- Host: GitHub
- URL: https://github.com/youssefhosni/awesome-ai-data-github-repos
- Owner: youssefHosni
- License: mit
- Created: 2023-05-09T02:02:22.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-27T13:37:28.000Z (about 2 months ago)
- Last Synced: 2024-10-27T15:18:05.082Z (about 2 months ago)
- Size: 1.6 MB
- Stars: 775
- Watchers: 20
- Forks: 170
- Open Issues: 1
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
Awesome Lists containing this project
- ultimate-awesome - awesome-ai-data-github-repos - A collection of the most important GitHub repos for ML, AI & Data science practitioners . (Programming Language Lists / Jupyter Notebook Lists)
README
# Awesome AI & Data GitHub-Repos [](https://awesome.re)
A curated list of the most essential GitHub repos that cover the AI & ML landscape. If you like to add or update projects, feel free to open an issue or submit a pull request. Contributions are very welcome!
[](https://github.com/youssefHosni/Awesome-ML-GitHub-Repos/blob/master/LICENSE)
[](https://GitHub.com/youssefHosni/Awesome-ML-GitHub-Repos/graphs/contributors/)
[](https://GitHub.com/youssefHosni/Awesome-ML-GitHub-Repos/issues/)
[](https://GitHub.com/youssefHosni/Awesome-ML-GitHub-Repos/pulls/)
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[](https://GitHub.com/youssefHosni/Awesome-ML-GitHub-Repos/watchers/)
[](https://GitHub.com/youssefHosni/Awesome-ML-GitHub-Repos/network/)
[](https://GitHub.com/youssefHosni/Awesome-ML-GitHub-Repos/stargazers/)
[](https://youssefh.substack.com/)
[](https://medium.com/@yousefhosni)
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[](https://www.youtube.com/channel/UCeEcSgRzYFuVt-2Yk1ULdhQ)
## Table of Contents:
* [Natural Language Processing (NLP)](https://github.com/youssefHosni/Awesome-ML-GitHub-Repos/blob/main/readme.md#:~:text=Data%20Engineering-,Natural%20Language%20Processing,-nlp%2Dtutorial%3A%20nlp)
* [Large Language Models(LLM)](https://github.com/youssefHosni/Awesome-ML-GitHub-Repos/blob/main/readme.md#:~:text=lines%20of%20code.-,Large%20Language%20Models,-Open%20LLMs%3A%20List)
* [Computer Vision](https://github.com/youssefHosni/Awesome-ML-GitHub-Repos/blob/main/readme.md#:~:text=LLMs%20through%20composability-,Computer%20Vision,-Computer%20Vision%20Tutorials)
* [Data Science](https://github.com/youssefHosni/Awesome-ML-GitHub-Repos/blob/main/readme.md#:~:text=based%20CV%20works-,Data%20Science,-Data%20Science%20for)
* [Machine Learning](https://github.com/youssefHosni/Awesome-ML-GitHub-Repos/blob/main/readme.md#:~:text=Interview%20Questions%20Answers-,Machine%20Learning,-Best%2Dof%20Machine)
* [Machine Learning Projects](https://github.com/youssefHosni/Awesome-AI-Data-GitHub-Repos/blob/main/readme.md#:~:text=Machine%20Learning%20Books-,Machine%20Learning%20Projects,-Orca%20calls%20Classifier)
* [Machine Learning Engineerings Operations (MLOps)](https://github.com/youssefHosni/Awesome-ML-GitHub-Repos/blob/main/readme.md#:~:text=Machine%20Learning%20Interviews-,Machine%20Learning%20Engineerings%20Operations%20(MLOps),-MLOps%2DBasics)
* [Data Engineering](https://github.com/youssefHosni/Awesome-ML-GitHub-Repos/blob/main/readme.md#:~:text=MLOps%20Course-,Data%20Engineering,-Data%20Engineering%20Zoomcamp)
* [SQL & Database](https://github.com/youssefHosni/Awesome-AI-Data-GitHub-Repos/blob/main/readme.md#:~:text=Engineering%20Interview%20Questions-,SQL%20%26%20Database,-SQL%20101%20by)
* [Statistics](https://github.com/youssefHosni/Awesome-AI-Data-GitHub-Repos#:~:text=Scientists%20by%20gvwilson-,Statistics,-Practical%20Statistics%20for)
## Natural Language Processing ##
* [nlp-tutorial](https://github.com/graykode/nlp-tutorial): nlp-tutorial is a tutorial for who is studying NLP(Natural Language Processing) using Pytorch. Most NLP models were implemented with less than 100 lines of code.
## Large Language Models ##
* [LLMs Practical Guide: The Practical Guides for Large Language Models](https://github.com/Mooler0410/LLMsPracticalGuide)
* [LLM Survey: A collection of papers and resources related to Large Language Models](https://github.com/RUCAIBox/LLMSurvey)
* [Open LLMs: List of LLMs that are all licensed for commercial](https://github.com/eugeneyan/open-llms)
* [Awesome LLM: Curated list of papers about large language models, especially relating to ChatGPT](https://github.com/Hannibal046/Awesome-LLM)
* [Awesome Decentralized LLM: Collection of LLM resources that can be used to build products you can "own" or to perform reproducible research](https://github.com/imaurer/awesome-decentralized-llm)
* [LangChain: Building applications with LLMs through composability](https://github.com/hwchase17/langchain)
* [Awesome LangChain: Curated list of tools and projects using LangChain](https://github.com/kyrolabs/awesome-langchain)
* [Awesome-Graph-LLM: A collection of AWESOME things about Graph-Related Large Language Models (LLMs).](https://github.com/XiaoxinHe/Awesome-Graph-LLM)
* [DemoGPT: Auto Gen-AI App Generator with the Power of Llama 2](https://github.com/melih-unsal/DemoGPT)
* [OpenLLM: An open platform for operating large language models (LLMs) in production](https://github.com/bentoml/OpenLLM)
* [LLM Zoo: democratizing ChatGPT](https://github.com/FreedomIntelligence/LLMZoo)
* [VectorDB-recipes](https://github.com/lancedb/vectordb-recipes)
* [Awesome GPT Prompt Engineering: A curated list of awesome resources, tools, and other shiny things for GPT prompt engineering](https://github.com/snwfdhmp/awesome-gpt-prompt-engineering)
* [Prompt Engineering Guide: ](https://github.com/dair-ai/Prompt-Engineering-Guide)
* [LLM Course](https://github.com/mlabonne/llm-course)
## Computer Vision ##
* [Awesome Computer Vision: A curated list of awesome computer vision resources](https://github.com/jbhuang0604/awesome-computer-vision)
* [Computer Vision Tutorials by Roboflow](https://github.com/roboflow/notebooks)
* [Transformer in Vision: paper list of some recent Transformer-based CV works](https://github.com/Yangzhangcst/Transformer-in-Computer-Vision)
* [Awesome-Referring-Image-Segmentation: A collection of referring image segmentation papers and datasets](https://github.com/MarkMoHR/Awesome-Referring-Image-Segmentation)
* [awesome-vision-language-pretraining-papers: Recent Advances in Vision and Language PreTrained Models (VL-PTMs)](https://github.com/yuewang-cuhk/awesome-vision-language-pretraining-papers)
* [Awesome Vision-and-Language: A curated list of awesome vision and language resources,](https://github.com/sangminwoo/awesome-vision-and-language)
* [Awesome-Temporal-Action-Detection-Temporal-Action-Proposal-Generation](https://github.com/zhenyingfang/Awesome-Temporal-Action-Detection-Temporal-Action-Proposal-Generation)
* [Awesome-Referring-Image-Segmentation: A collection of referring image segmentation papers and datasets.](https://github.com/MarkMoHR/Awesome-Referring-Image-Segmentation)
* [Awesome Masked Autoencoders: A collection of literature after or concurrent with Masked Autoencoder (MAE) ](https://github.com/EdisonLeeeee/Awesome-Masked-Autoencoders)
* [Awesome Visual-Transformer: Collection of some Transformer with Computer-Vision (CV) papers](https://github.com/dk-liang/Awesome-Visual-Transformer)
* [Transformer-Based Visual Segmentation: A Survey](https://github.com/lxtGH/Awesome-Segmentation-With-Transformer)
* [Awesome-Segmentation-With-Transformer](https://github.com/lxtGH/Awesome-Segmentation-With-Transformer)
* [CVPR 2o23 Paper with Code](https://github.com/amusi/CVPR2023-Papers-with-Code)
* [Awesome Deepfakes Detection](https://github.com/Daisy-Zhang/Awesome-Deepfakes-Detection)
* [Weekly-Top-Computer-Vision-Papers](https://github.com/youssefHosni/Weekly-Top-Computer-Vision-Papers)
## Data Science ##
* [Data Science for Beginners - A Curriculum](https://github.com/microsoft/Data-Science-For-Beginners)
* [Data Science Resources](https://github.com/jonathan-bower/DataScienceResources)
* [freeCodeCamp.org's open-source codebase and curriculum](https://github.com/freeCodeCamp/freeCodeCamp)
* [List of Data Science/Big Data Resources](https://github.com/chaconnewu/free-data-science-books)
* [Open Source Society University: Path to a free self-taught Education in Data Science](https://github.com/ossu/data-science)
* [AWESOME DATA SCIENCE: An open-source Data Science repository to learn and apply towards solving real-world problems.](https://github.com/academic/awesome-datascience)
* [Data Science ALL CHEAT SHEET](https://github.com/yash42828/Data-Science--All-Cheat-Sheet)
* [Data Science End-to-End Projects](https://github.com/veb-101/Data-Science-Projects)
* [Data Analysis Projects](https://github.com/arjunmann73/Data-Analytics-Projects)
* [Data Science Interview Resources](https://github.com/rbhatia46/Data-Science-Interview-Resources)
* [Data-Science Interview Questions Answers](https://github.com/youssefHosni/Data-Science-Interview-Questions-Answers)
* [Data-science-best-resources](https://github.com/tirthajyoti/Data-science-best-resources)
* [Amazing-Feature-Engineering](https://github.com/ashishpatel26/Amazing-Feature-Engineering)
* [Complete-Life-Cycle-of-a-Data-Science-Project](https://github.com/achuthasubhash/Complete-Life-Cycle-of-a-Data-Science-Project)
* [Data Science Cheatsheet](https://github.com/ml874/Data-Science-Cheatsheet)
* [PandasAI](https://github.com/gventuri/pandas-ai)
## Machine Learning ##
* [GitHub Community Discussions](https://github.com/community/community): In this repository, you will find categories for various product areas. Feel free to share feedback, discuss topics with other community members, or ask questions.
* [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning): A curated list of awesome machine learning frameworks, libraries and software (by language).
* [Machine Learning & Deep Learning Tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials): This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources
* [Best-of Machine Learning with Python](https://github.com/ml-tooling/best-of-ml-python): A ranked list of awesome machine learning Python libraries.
* [TensorFlow Examples](https://github.com/aymericdamien/TensorFlow-Examples): This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanations, for both TF v1 & v2.
* [Machine Learning Projects](https://github.com/lukas/ml-class)
* [Randy Olson's data analysis and machine learning projects](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects)
* [Minimum Viable Study Plan for Machine Learning Interviews](https://github.com/khangich/machine-learning-interview)
* [Machine Learning Interview Questions: Machine Learning and Computer Vision Engineer](https://github.com/andrewekhalel/MLQuestions)
* [Must Read Machine Learning & Deep Learning Papers](https://github.com/hurshd0/must-read-papers-for-ml)
* [Free Machine Learning Books](https://github.com/shahumar/Free-Machine-Learning-Books)
## Machine Learning Projects ##
* [Orca calls Classifier Project](https://github.com/rohankrgupta/Orca-call-Classifier-Machine-learning)
* [Multi-Modal House Price Estimation](https://github.com/Mehrab-Kalantari/Multi-Modal-House-Price-Estimation)
* [Movie Recommendation System Project](https://github.com/Mehrab-Kalantari/Multi-Modal-House-Price-Estimation)
* [Land Cover Semantic Segmentation Project](https://github.com/souvikmajumder26/Land-Cover-Semantic-Segmentation-PyTorch)
* [Music Recommender System using ALS Algorithm with Apache Spark and Python](https://github.com/ramyananth/Music-Recommender-System-using-ALS-Algorithm-with-Apache-Spark-and-Python)
* [Adversarial Task](https://github.com/antonio-f/Adversarial-Task)
* [Flowers Classification](https://github.com/firaja/flowers-classification)
* [99 Machine Learning Projects](https://github.com/gimseng/99-ML-Learning-Projects)
* [Advanced Machine Learning Projects I](https://github.com/beimingliu/AdvancedMachineLearning)
* [Advanced Machine Learning II](https://github.com/mohammadmozafari/advanced-machine-learning)
## Machine Learning Engineering Operations (MLOps) ##
* [MLOps Zoomcamp](https://github.com/DataTalksClub/mlops-zoomcamp)
* [MLOps-Basics](https://github.com/graviraja/MLOps-Basics)
* [MLOps Guide](https://mlops-guide.github.io/)
* [Awesome MLOps](https://github.com/visenger/awesome-mlops)
* [Awesome MLOps - Tools](https://github.com/kelvins/awesome-mlops)
* [DTU MLOps](https://github.com/SkafteNicki/dtu_mlops)
* [MLOps Course](https://github.com/GokuMohandas/mlops-course)
## Data Engineering ##
* [Data Engineering Zoomcamp](https://github.com/DataTalksClub/data-engineering-zoomcamp)
* [Data Engineering Cookbook](https://github.com/andkret/Cookbook)
* [How To Become a Data Engineer](https://github.com/adilkhash/Data-Engineering-HowTo)
* [Awesome Data Engineering](https://github.com/igorbarinov/awesome-data-engineering)
* [Data Engineering Roadmap](https://github.com/datastacktv/data-engineer-roadmap)
* [Data Engineering Projects](https://github.com/alanchn31/Data-Engineering-Projects)
* [Data Engineering Interview Questions](https://github.com/OBenner/data-engineering-interview-questions)
## SQL & Database ##
* [SQL 101 by s-shemmee]()
* [Learn SQL by WebDevSimplified]()
* [SQL Masterclass by datawithdanny]()
* [SQL Map by sqlmapproject]()
* [SQL Server Samples by Microsoft]()
* [SQL Music Store Analysis Project by Rishabhnmishra]()
* [Data Engineering Zoomcamp by DataTalksClub]()
* [SQL Server Kit by ktaranov]()
* [Awesome DB Tools by mgramin]()
* [SQL for Wary Data Scientists by gvwilson]()
## Statistics ##
* [Practical Statistics for Data Scientists](https://github.com/gedeck/practical-statistics-for-data-scientists)
* [Probabilistic Programming and Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)
* [Statsmodels: Statistical Modeling and Econometrics in Python](https://github.com/statsmodels/statsmodels)
* [TensorFlow Probability](https://github.com/tensorflow/probability)
* [The Probability and Statistics Cookbook](https://github.com/mavam/stat-cookbook)
* [Seeing Theory](https://github.com/seeingtheory/Seeing-Theory)
* [Stats Maths with Python](https://github.com/tirthajyoti/Stats-Maths-with-Python)
* [Python for Probability, Statistics, and Machine Learning](https://github.com/unpingco/Python-for-Probability-Statistics-and-Machine-Learning)
* [Probability and Statistics VIP Cheatsheets](https://github.com/shervinea/stanford-cme-106-probability-and-statistics)
* [Basic Mathematics for Machine Learning](https://github.com/hrnbot/Basic-Mathematics-for-Machine-Learning)