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https://github.com/tirendazacademy/awesome-data-science-resources

Resources about data science, machine learning, deep learning, data engineering, and SQL.
https://github.com/tirendazacademy/awesome-data-science-resources

List: awesome-data-science-resources

ai artificial-intelligence data-analysis data-engineering data-science dataengineering datascience deep-learning deeplearning machine-learning machinelearning machinelearning-python sql

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Resources about data science, machine learning, deep learning, data engineering, and SQL.

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README 

        
# 🔶 Welcome to Awesome Data Science Resources 🔶



![](https://github.com/TirendazAcademy/Awesome-Data-Science-Resources/blob/main/Images/Banner.png?raw=true)



Table of contents:



- [Data science](#data-science)

- [Machine learning](#machine-learning)

- [ML engineering](#ml-engineering)

- [Deep learning](#deep-learning)

- [Generative AI](#gen-ai)

- [NLP](#nlp)

- [Data engineering](#data-engineering)

- [SQL](#sql)



If you like this repo, give us a ⭐



---





# 🚀 DATA SCIENCE

![](https://github.com/TirendazAcademy/Awesome-Data-Science-Resources/blob/main/Images/Data-Science.png)



### Free Books



- [Free Programming Books](https://github.com/EbookFoundation/free-programming-books)

- [Natural Language Processing with Python](https://nltk.org/book_1ed/) 

- [Think Stats](https://greenteapress.com/wp/think-stats-2e/) 

- [Bayesian Methods for Hackers](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) 

- [R for Data Science](https://r4ds.had.co.nz) 

- [Machine Learning Yearning](https://github.com/ajaymache/machine-learning-yearning) 

- [Hands-on Machine Learning with Scikit-learn and Tensorflow](https://www.oreilly.com/library/view/hands-on-machine-learning/9781491962282/)

- [Forecasting: Principles and Practice](https://otexts.com/fpp2/index.html) 

- [Deep learning](https://deeplearningbook.org) 

- [Linear Algebra](https://joshua.smcvt.edu/linearalgebra/) 

- [Introduction to Machine Learning with Python](https://drive.google.com/file/d/10Vrml277NCOa6SS9GV10m847jtPynt_n/view)



### Blogs



- [Towards Data Science](https://towardsdatascience.com/)

- [KD nuggets](https://www.kdnuggets.com/)

- [HuggingFace Blog](https://huggingface.co/blog)

- [Comet Blog](https://www.comet.com/site/blog/)

- [Assembly AI](https://www.assemblyai.com/blog/)

- [Geeks for Geeks](https://www.geeksforgeeks.org/)

- [Weights & Biases Blog](https://wandb.ai/fully-connected)

- [Analytics Vidhya](https://www.analyticsvidhya.com/blog/)

- [Datacamp Blog](https://www.datacamp.com/blog)

- [Papers with code](https://paperswithcode.com/)

- [Aman.ai-exploring the art of AI](https://aman.ai/)



### Projects



- [Different project topics for learning purposes](https://github.com/veb-101/Data-Science-Projects)

- [Online challenge](https://github.com/alexattia/Data-Science-Projects)

- [Data science challenges are made on Kaggle using Python](https://github.com/alexattia/Data-Science-Projects)

- [Data science projects](https://github.com/hyunjoonbok/Python-Projects)

- [Kaggle Data science projects](https://github.com/alexattia/Data-Science-Projects)



### Repos Related to Libraries



- [Awesome Pandas](https://github.com/tommyod/awesome-pandas)

- [Pandas Tutorial](https://github.com/KeithGalli/pandas)

- [Effective Pandas](https://github.com/TomAugspurger/effective-pandas)

- [Pandas Tutorial Videos](https://github.com/justmarkham/pandas-videos)

- [Pandas AI](https://github.com/gventuri/pandas-ai)

- [Pandas-Workshop](https://github.com/stefmolin/pandas-workshop/tree/936e5dea93e1d60efbf98b641050c4ad1f3f1819)



### Tools



- [lazypredict](https://github.com/shankarpandala/lazypredict) 

- [PyCaret](https://github.com/pycaret/pycaret) 

- [FeatureSelectionGA](https://github.com/kaushalshetty/FeatureSelectionGA) 

- [Elicit](https://elicit.org/)



### Useful Repos



- [Kaggle Courses](https://github.com/drakearch/kaggle-courses)

- [Pattern Classification](https://github.com/rasbt/pattern_classification) 

- [Feature Selector: Simple Feature Selection in Python](https://github.com/WillKoehrsen/feature-selector)

- [Scikit Learn Mooc](https://inria.github.io/scikit-learn-mooc/index.html) 

- [Hands-on Machine Learning & Deep Learning ](https://github.com/bansalkanav/Machine_Learning_and_Deep_Learning)

- [Data Science Best Resources](https://github.com/tirthajyoti/Data-science-best-resources)

- [Stats and Maths with Python](https://github.com/tirthajyoti/Stats-Maths-with-Python)

- [](https://github.com/nivu/ai_all_resources)



### DataSets



- [A dataset with political datasets](https://github.com/erikgahner/PolData)



###  Articles You Need to Read



- [Transformers](https://arxiv.org/pdf/1706.03762.pdf) 

- [BERT](https://arxiv.org/pdf/1810.04805.pdf) 

- [StyleGAN](https://arxiv.org/pdf/1812.04948.pdf)

- [CLIP](https://arxiv.org/pdf/2103.00020.pdf) 

- [The game of Go with deep neural networks](https://storage.googleapis.com/deepmind-media/alphago/AlphaGoNaturePaper.pdf)

- [DNN for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf) 





# 🚀 MACHINE LEARNING

![](https://github.com/TirendazAcademy/Awesome-Data-Science-Resources/blob/main/Images/Machine-Learning.png)



### Free Books



- [Python Data Science Handbook by Jake VanderPlas](https://github.com/jakevdp/PythonDataScienceHandbook)

- [Neural Networks and Deep Learning by Michael Nielsen](http://neuralnetworksanddeeplearning.com/)

- [Think Bayes by Allen B. Downey](https://greenteapress.com/wp/think-bayes/)

- [Machine Learning & Big Data by Kareem Alkaseer](http://www.kareemalkaseer.com/books/ml)

- [Statistical Learning with Sparsity The Lasso and Generalizations by Trevor Hastie](http://www.kareemalkaseer.com/books/ml)

- [ML Interviews Book](https://huyenchip.com/ml-interviews-book/)

- [Machine Learning Simplified](https://themlsbook.com/)



### Courses



- [Production Machine Learning Systems](https://www.coursera.org/learn/gcp-production-ml-systems?specialization=preparing-for-google-cloud-machine-learning-engineer-professional-certificate)

- [Google Cloud Big Data and Machine Learning Fundamentals](https://www.coursera.org/learn/gcp-big-data-ml-fundamentals?specialization=preparing-for-google-cloud-machine-learning-engineer-professional-certificate)

- [MLOps (Machine Learning Operations) Fundamentals](https://www.coursera.org/learn/mlops-fundamentals?specialization=preparing-for-google-cloud-machine-learning-engineer-professional-certificate)

- [ML Pipelines on Google Cloud](https://www.coursera.org/learn/mlops-fundamentals?specialization=preparing-for-google-cloud-machine-learning-engineer-professional-certificate)



### Useful Repos



- [Machine Learning for Beginners](https://github.com/microsoft/ML-For-Beginners) - 

- [100 Days Of ML Code](https://github.com/Avik-Jain/100-Days-Of-ML-Code) 

- [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) 

- [Project-Based Learning](https://github.com/practical-tutorials/project-based-learning) 

- [Homemade Machine Learning](https://github.com/trekhleb/homemade-machine-learning) 

- [500 AI & Machine-learning Projects with Code](https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code) 

- [Principles of Machine Learning Python by MicrosoftLearning](https://github.com/MicrosoftLearning/Principles-of-Machine-Learning-Python)

- [Machine Learning Basics](https://github.com/zotroneneis/machine_learning_basics)

- [TOBB ETU Machine Learning Course](https://github.com/bbardakk/TOBB-ETU-YAP470-2022)

- [DataCamp Notebooks](https://github.com/ozlerhakan/datacamp)

- [Machine Learning Guide](https://github.com/mikeroyal/Machine-Learning-Guide)

- [Practical Machine Learning with Python](https://github.com/dipanjanS/practical-machine-learning-with-python)

- [Machine Learning Pathway](https://github.com/ayyucedemirbas/Machine-Learning-Pathway)



### Projects



- [ML projects](https://github.com/cloudxlab/ml)

- [DataCamp Project Solutions Python](https://github.com/veeralakrishna/DataCamp-Project-Solutions-Python)

- [Machine Learning Projects](https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects)

- [Data Analytics Projects](https://github.com/arjunmann73/Data-Analytics-Projects)



### Tools



- [PyCaret](https://github.com/pycaret/pycaret)

- [EvalML](https://github.com/alteryx/evalml)





# 🚀 ML ENGINEERING

![](https://github.com/TirendazAcademy/Awesome-Data-Science-Resources/blob/main/Images/ML-Engineering.png)



### Useful Repos



- [MLOps Zoomcamp](https://github.com/DataTalksClub/mlops-zoomcamp)



### MLOps Tools



- [MLflow](https://mlflow.org/)

- [Comet ML](https://www.comet.com/site/)

- [Weights & Biases ](https://github.com/wandb)

- [ML Run](https://github.com/mlrun/mlrun)



### YouTube



- [Python Machine Learning Tutorial](https://youtu.be/7eh4d6sabA0)





# 🚀 DEEP LEARNING

![](https://github.com/TirendazAcademy/Awesome-Data-Science-Resources/blob/main/Images/Deep-Learning.png)



### Books 



- [Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646)

- [Machine Learning with PyTorch and Scikit-Learn](https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312)

- [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python)

- [AI and Machine Learning for Coders](https://www.amazon.com/Machine-Learning-Coders-Programmers-Intelligence/dp/1492078190)

- [Deep Learning for Coders with fastai & PyTorch](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527)

- [Deep Learning from Scratch](https://www.amazon.com/Deep-Learning-Scratch-Building-Principles/dp/1492041416)

- [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning)

- [Fundamentals of Deep Learning](https://www.oreilly.com/library/view/fundamentals-of-deep/9781492082170/)

- [Deep Learning with TensorFlow and Keras](https://www.amazon.com/Deep-Learning-TensorFlow-Keras-reinforcement/dp/1803232919)

- [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618)



### Courses



- [Practical Deep Learning](https://course.fast.ai/)

- [Deep Learning Fundamentals](https://lightning.ai/pages/courses/deep-learning-fundamentals/)

- [Neuromatch deep learning course](https://deeplearning.neuromatch.io/tutorials/intro.html)



### Frameworks



- [TensorFlow](https://www.tensorflow.org/tutorials)

- [Keras](https://keras.io/)

- [PyTorch](https://pytorch.org/tutorials/)

- [A curated list of tutorials](https://github.com/ritchieng/the-incredible-pytorch)

- [Learn PyTorch](https://www.learnpytorch.io/)

- [FastAI](https://docs.fast.ai/)

- [Pytorch Lightning](https://pytorch-lightning.readthedocs.io/en/stable/)



### Repos



- [FastAI Course](https://github.com/fastai/courses)

- [Computer Vision](https://github.com/sindresorhus/awesome#computer-science)

- [Awesome-TensorFlow](https://github.com/jtoy/awesome-tensorflow)

- [Awesome AI Courses](https://github.com/SkalskiP/courses)

- [Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning)

- [AI-For-Beginners ](https://github.com/microsoft/AI-For-Beginners)

- [Computer Vision Lectures](https://github.com/khanhnamle1994/computer-vision)

- [Everything about Transfer Learning](https://github.com/jindongwang/transferlearning)

- [Pytorch Beginner](https://github.com/L1aoXingyu/pytorch-beginner)

- [Variational autoencoder for anomaly detection](https://github.com/Michedev/VAE_anomaly_detection)



### YouTube



- [PyTorch for Deep Learning & Machine Learning – Full Course](https://youtu.be/V_xro1bcAuA)

- [PyTorch Crash Course](https://youtu.be/OIenNRt2bjg)

- [Object Detection](https://youtu.be/yqkISICHH-U)

- [Deep Learning with PyTorch](https://youtu.be/c36lUUr864M)

- [Reinforcement Learning](https://youtu.be/Mut_u40Sqz4)





# 🚀 Generative AI



- [LLM Courses](https://github.com/mlabonne/llm-course)

- [Awesome Generative AI Guide](https://github.com/aishwaryanr/awesome-generative-ai-guide)

- [Stable diffusion with Keras with Pokemon Dataset](https://github.com/sayakpaul/stable-diffusion-keras-ft)

- [Expert-Level Tutorials on Stable Diffusion](https://github.com/FurkanGozukara/Stable-Diffusion))

- [Awesome ChatGPT](https://github.com/sindresorhus/awesome-chatgpt)





# 🚀 NLP

![](https://github.com/TirendazAcademy/Awesome-Data-Science-Resources/blob/main/Images/NLP.png)



### Repos



- [Natural Language Processing Tutorial](https://github.com/graykode/nlp-tutorial) 

- [NLP Recipes](https://github.com/microsoft/nlp-recipes) 

- [NLP Course](https://github.com/yandexdataschool/nlp_course) 

- [NLP in Python Tutorial](https://github.com/adashofdata/nlp-in-python-tutorial) 

- [Awesome NLP](https://github.com/keon/awesome-nlp) 

- [Deep Learning Drizzle](https://deep-learning-drizzle.github.io/) 



### Courses



- [Advanced NLP with spaCy](https://course.spacy.io/en)



### LLMs



- [Building applications with LLMs](https://docs.langchain.com/docs/)

- [Awesome LangChain](https://github.com/kyrolabs/awesome-langchain)

- [Overview - Tutorial - Examples of LangChain](https://github.com/gkamradt/langchain-tutorials)

- [Share LLM apps](https://docs.chainlit.io/overview)

- [FlagAI (Fast LArge-scale General AI models)](https://github.com/FlagAI-Open/FlagAI)

- [OpenAI CookBook](https://github.com/openai/openai-cookbook)

- [Code Basics LangChain Tutorials](https://github.com/codebasics/langchain)

- [Cohere LLM University](https://docs.cohere.com/docs/llmu)

- [Conversation QA Gradio](https://github.com/hwchase17/conversation-qa-gradio)





# 🚀 DATA ENGINEERING

![](https://github.com/TirendazAcademy/Awesome-Data-Science-Resources/blob/main/Images/Data-Engineering.png)



### Repos



- [The Data Engineering Cookbook](https://github.com/oleg-agapov/data-engineering-book)

- [Data Engineering Zoomcamp](https://github.com/DataTalksClub/data-engineering-zoomcamp) 

- [Awesome Data Engineering](https://github.com/igorbarinov/awesome-data-engineering) 

- [Udacity Data Engineering Projects](https://github.com/san089/Udacity-Data-Engineering-Projects) 

- [Data Engineering Practice](https://github.com/danielbeach/data-engineering-practice) 

- [Awesome Opensource Data Engineering](https://github.com/gunnarmorling/awesome-opensource-data-engineering) 

- [Data Engineering Zoomcamp](https://github.com/DataTalksClub/data-engineering-zoomcamp)

- [How To Become a Data Engineer](https://github.com/adilkhash/Data-Engineering-HowTo)



### Projects



- [HashtagCashtag](https://github.com/shafiab/HashtagCashtag) 

- [Building a Data Engineering Project in 20 Minutes](https://github.com/sspaeti-com/practical-data-engineering) 

- [Analyzing GitHub Repos](https://hoffa.medium.com/400-000-github-repositories-1-billion-files-14-terabytes-of-code-spaces-or-tabs-7cfe0b5dd7fd#.qm2s97y25) 

- [Web Crawler For Online Inflation](https://github.com/uhussain/WebCrawlerForOnlineInflation) 

- [This repo](https://github.com/alanchn31/Data-Engineering-Projects) 

- [Data Engineering Project](https://github.com/damklis/DataEngineeringProject) 

- [Data Engineering Practice](https://github.com/danielbeach/data-engineering-practice)  





# 🚀 SQL

![](https://github.com/TirendazAcademy/Awesome-Data-Science-Resources/blob/main/Images/SQL.png)



### Free Resources



- [SQLZoo](https://sqlzoo.net/wiki/SQL_Tutorial) 

- [SQLBolt](https://sqlbolt.com)

- [Kaggle](https://www.kaggle.com/learn/intro-to-sql)

- [CodeAcademy](https://join.codecademy.com/learn/learn-sql/) 

- [Pop SQL](https://popsql.com/learn-sql) 

- [Learning SQL book](http://www.r-5.org/files/books/computers/languages/sql/mysql/Alan_Beaulieu-Learning_SQL-EN.pdf) 

- [Khan Academy](https://www.khanacademy.org/computing/computer-programming/sql/sql-basics/v/welcome-to-sql) 



### Projects



- [Data Analysis Projects](https://github.com/codebasics/DataAnalysisProjects)



---



🚀 We'll update this repo once I find new sources so don't forget to watch this repo. If you enjoy this repo, give us a ⭐ and share 🙏



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