Machine Learning Open Source University
<p align="center">
    <br>
    <img src="https://github.com/d0r1h/ML-University/blob/master/ml_logo.png" width="300"/>
    <br>
<p>

<p align="center">
 <a href="https://hits.seeyoufarm.com"><img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Fd0r1h%2FML-University&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false"/></a>
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  </a>
</p>
<h3 align="center">
    <p>A Free Machine Learning University </p>
</h3>
<br>

Machine Learning Open Source University is an IDEA of free-learning of a ML enthusiast for all other ML enthusiast

**This list is continuously updated** - And if you are a Ml practitioner and have some good suggestions to improve this or have somegood resources to share, you create pull request and contribute.


**Table of Contents**

1. [Getting Started](#getting-started)
2. [Mathematics](#mathematics)
3. [Machine Learning](#machine-learning)
4. [Deep Learning](#deep-learning)
5. [Natural language processing](#natural-language-processing)
6. [Reinforcement learning](#reinforcement-learning)
7. [Books](#books)
8. [ML in Production](#ml-in-production)
9. [Quantum ML](#quantum-ml)
10. [DataSets](#datasets)
11. [Other Useful Websites](#other-useful-websites)
12. [Other Useful GitRrpo](#other-useful-gitrepo)
13. [Blogs and Webinar](#blogs-and-webinar) 
14. [Must Read Research Paper](#must-read-research-paper)
15. [Company Tech Blogs](#company-tech-blogs)









## Getting Started

 | Title and Source                                             | Link                               				          |
 |------------------------------------------------------------  | -------------------------------------------------------------|
 | Elements of AI :  Part-1                                     | [WebSite](https://course.elementsofai.com/)				  |
 | Elements of AI :  Part-2                                     | [WebSite](https://buildingai.elementsofai.com/) 			  |
 | CS50’s Introduction to AI	**Harvard**			            | [Cs50 WebSite](https://cs50.harvard.edu/ai/2020/)			  |
 | Intro to Computational Thinking and Data Science **MIT**     | [WebSite](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/)
 | Practical Data Ethics										| [fast.ai](https://ethics.fast.ai/)
 | Machine learning Mastery Getting Started 					| [machinelearningmastery](https://machinelearningmastery.com/start-here/)
 | Design and Analysis of Algorithms **MIT**					| [ocw.mit.edu](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2015/)
 | AI: Principles and Techniques **Stanford** 					| [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rO1NB9TD4iUZ3qghGEGtqNX)|
 | The Private AI Series 										| [openmined](https://courses.openmined.org/courses)|

 

## Mathematics


 | Title and Source                                             | Link                               				          |
 |------------------------------------------------------------  | -------------------------------------------------------------
 | Statistics in Machine Learning (Krish Naik)                  | [YouTube](https://www.youtube.com/playlist?list=PLZoTAELRMXVMhVyr3Ri9IQ-t5QPBtxzJO)
 | Computational Linear Algebra for Coders						| [fast.ai](https://github.com/fastai/numerical-linear-algebra/blob/master/README.md)
 | Linear Algebra  **MIT**										| [WebSite](https://openlearninglibrary.mit.edu/courses/course-v1:OCW+18.06SC+2T2019/course/)|
 | Statistics by zstatistics									| [WebSite](https://www.zstatistics.com/videos)|
 | Essence of linear algebra by 3Blue1Brown						| [YouTube](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)|
 | SEEING THEORY (Visual Probability)	**brown**    		    | [WebSite](https://seeing-theory.brown.edu/basic-probability/index.html)|
 | Matrix Methods in Data Analysis,and Machine Learning **MIT** | [WebSite](https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/)
 | Math for Machine Learning 									| [YouTube](https://www.youtube.com/playlist?app=desktop&list=PLD80i8An1OEGZ2tYimemzwC3xqkU0jKUg) |
 | Statistics for Applications **MIT** | [YouTube](https://www.youtube.com/playlist?list=PLUl4u3cNGP60uVBMaoNERc6knT_MgPKS0) 



## Machine Learning

 | Title and Source                                             | Link                               				           |
 |------------------------------------------------------------  | -------------------------------------------------------------|
 | Introduction to Machine Learning with scikit-learn 			| [dataschool](https://courses.dataschool.io/introduction-to-machine-learning-with-scikit-learn)|
 | Introduction to Machine Learning								| [sebastianraschka](https://sebastianraschka.com/blog/2021/ml-course.html)
 | Open Machine Learning Course 								| [mlcourse.ai](https://mlcourse.ai/)						   |
 | Machine Learning (CS229) **Stanford**						| [WebSite](http://cs229.stanford.edu/syllabus-spring2020.html) [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)|
 | Introduction to Machine Learning **MIT** 					| [WebSite](https://tinyurl.com/ybl6udcr)					   |
 | Machine Learning Systems Design 2021 (CS329S) **Stanford**   | [WebSite](https://stanford-cs329s.github.io/syllabus.html)   |
 | Applied Machine Learning 2020 (CS5787) **Cornell Tech**      | [YouTube](https://www.youtube.com/playlist?list=PL2UML_KCiC0UlY7iCQDSiGDMovaupqc83)
 | Machine Learning for Healthcare **MIT** 						| [WebSite](https://tinyurl.com/yxgeesdf)					   |
 | Machine Learning for Trading **Georgia Tech**				| [WebSite](https://lucylabs.gatech.edu/ml4t/)				   |	
 | Introduction to Machine Learning for Coders					| [fast.ai](https://course18.fast.ai/ml.html)
 | Machine Learning Crash Course								| [Google AI](https://developers.google.com/machine-learning/crash-course)|
 | Machine Learning with Python 								| [freecodecamp](https://www.freecodecamp.org/learn/machine-learning-with-python/)|
 | Deep Reinforcement Learning:CS285 **UC Berkeley**			| [YouTube](https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc)|
 | Probabilistic Machine Learning **University of Tübingen**    | [YouTube](https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd)|
 | Machine Learning with Graphs(CS224W) **Stanford** 			| [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn)|
 | Machine Learning in Production **CMU**						| [WebSite](https://ckaestne.github.io/seai/)|
 | Machine Learning & Deep Learning Fundamentals                | [deeplizard](https://deeplizard.com/learn/video/gZmobeGL0Yg)|
 | Interpretability and Explainability in Machine Learning      | [WebSite](https://interpretable-ml-class.github.io/)|
 | Practical Machine Learning 2021 **Stanford**					| [WebSite](https://c.d2l.ai/stanford-cs329p/index.html#)|
 | Machine Learning **VU University** 							| [WebSite](https://mlvu.github.io/)|
 | Machine Learning for Cyber Security **Purdue University**    | [YouTube](https://www.youtube.com/playlist?list=PL74sw1ohGx7GHqDHCkXZeqMQBVUTMrVLE)|
 | Audio Signal Processing for Machine Learning 				| [YouTube](https://www.youtube.com/playlist?list=PL-wATfeyAMNqIee7cH3q1bh4QJFAaeNv0)|
 | Machine learning & causal inference **Stanford**				| [YouTube](https://www.youtube.com/playlist?list=PLxq_lXOUlvQAoWZEqhRqHNezS30lI49G-)|
 | Machine learning cs156 **caltech**                           | [YouTube](https://www.youtube.com/playlist?list=PLD63A284B7615313A) |
 | Multimodal machine learning (MMML) **CMU**                   | [WebSite](https://cmu-multicomp-lab.github.io/mmml-course/fall2020/)  [YouTube](https://www.youtube.com/playlist?list=PL-Fhd_vrvisNup9YQs_TdLW7DQz-lda0G) | 
 	

## Deep Learning
 
 
 | Title and Source                                             | Link                               				           |
 |------------------------------------------------------------  | -------------------------------------------------------------|
 | Introduction to Deep Learning(6.S191) **MIT**		 		| [YouTube](https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI)					   |
 | Introduction to Deep Learning								| [sebastianraschka](https://sebastianraschka.com/blog/2021/dl-course.html)
 | Deep Learning **NYU**					 					| [WebSite](https://atcold.github.io/pytorch-Deep-Learning/)  [2021](https://atcold.github.io/NYU-DLSP21/) |
 | Deep Learning (CS182) **UC Berkeley**						| [YouTube](https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A)
 | Deep Learning Lecture Series	**DeepMind x UCL**			    | [YouTube](https://www.youtube.com/playlist?list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF)|
 | Deep Learning (CS230) **Stanford**						    | [WebSite](https://cs230.stanford.edu/lecture/)               | 
 | CNN for Visual Recognition(CS231n) **Stanford**    		    | [WebSite-2020](https://cs231n.github.io/)  [YouTube-2017](https://tinyurl.com/y2gghbvs)|
 | Full Stack Deep Learning   									| [WebSite](https://course.fullstackdeeplearning.com/)[2021](https://fullstackdeeplearning.com/spring2021/)|
 | Practical Deep Learning for Coders, v3                       | [fast.ai](https://course19.fast.ai/index.html)			   |
 | Deep Learning Crash Course 2021 d2l.ai 						| [YouTube](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQsDaBNtcFwMQuJw_djFnbd)|
 | Deep Learning for Computer Vision **Michigan**				| [WebSite](https://web.eecs.umich.edu/~justincj/teaching/eecs498/FA2020/)|
 | Neural Networks from Scratch in Python by Sentdex			| [YouTube](https://www.youtube.com/playlist?app=desktop&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3)|
 | Keras - Python Deep Learning Neural Network API				| [deeplizard](https://deeplizard.com/learn/video/RznKVRTFkBY)|
 | Reproducible Deep Learning									| [sscardapane.it](https://www.sscardapane.it/teaching/reproducibledl/)|
 | PyTorch Fundamentals 										| [microsoft](https://docs.microsoft.com/en-us/learn/paths/pytorch-fundamentals/)|
 | Geometric Deep Learing (GDL100)								| [geometricdeeplearning](https://geometricdeeplearning.com/lectures/)|
 | Deep learning Neuromatch Academy 							| [neuromatch](https://deeplearning.neuromatch.io/tutorials/intro.html)
 | Deep Learning for Molecules and Materials					| [WebSite](https://whitead.github.io/dmol-book/intro.html)|
 | Deep Learning course for Vision								| [arthurdouillard.com](https://arthurdouillard.com/deepcourse/)|
 | Deep Multi-Task and Meta Learning (CS330) **Stanford**  		| [WebSite](https://cs330.stanford.edu/) [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5)|
 | Deep Learning Interviews book 								| [WebSite](https://github.com/BoltzmannEntropy/interviews.ai)|
 | Deep Learning for Computer Vision 2021                       | [YouTube](https://www.youtube.com/playlist?list=PL_Z2_U9MIJdNgFM7-f2fZ9ZxjVRP_jhJv)
 | Deep Learning 2022 **CMU**                                   | [YouTube](https://www.youtube.com/playlist?list=PLp-0K3kfddPxRmjgjm0P1WT6H-gTqE8j9)   


## Natural language processing 

 | Title and Source                                             | Link                               				  		   |
 | ------------------------------------------------------------ | -----------------------------------------------------------|
 | Natural Language Processing AWS								| [YouTube](https://www.youtube.com/playlist?list=PL8P_Z6C4GcuWfAq8Pt6PBYlck4OprHXsw)
 | NLP - Krish Naik 				                            | [YouTube](https://www.youtube.com/playlist?list=PLZoTAELRMXVMdJ5sqbCK2LiM0HhQVWNzm)
 | NLP with Deep Learning(CS224N) 2019 **Stanford**     		| [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z) [2021](https://www.youtube.com/playlist?list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ)
 | A Code-First Introduction to Natural Language Processing     | [fast.ai](https://www.fast.ai/2019/07/08/fastai-nlp/)|
 | CMU Neural Nets for NLP 2021  **Carnegie Mellon University** | [YouTube](https://www.youtube.com/playlist?list=PL8PYTP1V4I8AkaHEJ7lOOrlex-pcxS-XV)|
 | Speech and Language Processing **Stanford** 					| [WebSite](https://web.stanford.edu/~jurafsky/slp3/) |
 | Natural Language Understanding (CS224U) **Stanford**			| [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rObpMCir6rNNUlFAn56Js20) [2022](https://web.stanford.edu/class/cs224u/)
 | NLP with Dan Jurafsky and Chris Manning, 2012 **Stanford**   | [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rOFZnDyrlW3-nI7tMLtmiJZ)|
 | Intro to NLP with spaCy   									| [YouTube](https://www.youtube.com/playlist?list=PLBmcuObd5An559HbDr_alBnwVsGq-7uTF)|
 | Advanced NLP with spaCy 										| [website](https://course.spacy.io/en/)                                             |
 | Applied Language Technology 									| [website](https://applied-language-technology.readthedocs.io/en/latest/)|
 | Advanced Natural Language Processing **Umass**				| [website](https://people.cs.umass.edu/~miyyer/cs685/schedule.html) [YouTube 2020](https://www.youtube.com/playlist?list=PLWnsVgP6CzadmQX6qevbar3_vDBioWHJL)|
 | Huggingface Course											| [huggingface.co](https://huggingface.co/course/chapter1?fw=tf)|
 | NLP Course **Michigan**										| [github](https://github.com/deskool/nlp-class)|
 | Multilingual NLP 2020 **CMU**								| [YouTube](https://www.youtube.com/playlist?list=PL8PYTP1V4I8CHhppU6n1Q9-04m96D9gt5)|
 | Advanced NLP 2021 **CMU**									| [YouTube](https://www.youtube.com/playlist?list=PL8PYTP1V4I8AYSXn_GKVgwXVluCT9chJ6)|
 | Transformers United **stanford**                             | [Website](https://web.stanford.edu/class/cs25/)  [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM) |  

  

## Reinforcement learning

 | Title and Source                                             | Link														                                         |
 |------------------------------------------------------------  | -----------------------------------------------------------|
 | Reinforcement Learning(CS234)  **Stanford** 					| [YouTube-2019](https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u)|
 | Introduction to reinforcement learning **DeepMind**			| [YouTube-2015](https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ)|
 | Reinforcement Learning Course  **DeepMind & UCL**			| [YouTube-2018](https://www.youtube.com/playlist?list=PLqYmG7hTraZBKeNJ-JE_eyJHZ7XgBoAyb)|
 | Advanced Deep Learning & Reinforcement Learning        		| [YouTube](https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs)|
 | DeepMind x UCL Reinforcement Learning 2021					| [YouTube](https://www.youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm)
 
 
## Books


 | Title and Source                                             | Link                               				         |
 |------------------------------------------------------------  | -----------------------------------------------------------|
 | Scientific Python Lectures		 							| [ScipyLectures](https://scipy-lectures.org/_downloads/ScipyLectures-simple.pdf)|
 | Mathematics for Machine Learning							    | [mml-book](https://mml-book.github.io/book/mml-book.pdf)	 |
 | An Introduction to Statistical Learning                      | [statlearning](https://www.statlearning.com/)              |
 | Think Stats 													| [Think Stats](https://greenteapress.com/wp/think-stats-2e/)|
 | Python Data Science Handbook                                 | [Python For DS](https://jakevdp.github.io/PythonDataScienceHandbook/)|
 | Natural Language Processing with Python - NLTK               | [NLTK](https://www.nltk.org/book/)						 |
 | Deep Learning by Ian Goodfellow             					| [deeplearningbook](https://www.deeplearningbook.org/)		 |
 | Dive into Deep Learning 										| [d2l.ai](https://d2l.ai/index.html)
 | Approaching (Almost) Any Machine Learning Problem    		| [AAANLP](https://github.com/abhishekkrthakur/approachingalmost/blob/master/AAAMLP.pdf)|
 | Neural networks and Deep learning							| [neuralnetworksanddeeplearning](http://neuralnetworksanddeeplearning.com/index.html)|
 | AutoML: Methods, Systems, Challenges (first book on AutoML)  | [automl](https://www.automl.org/book/)|
 | Feature Engineering and Selection	 						| [bookdown.org](https://bookdown.org/max/FES/)|
 | Introduction to Machine Learning Interviews Book				| [huyenchip.com](https://huyenchip.com/ml-interviews-book/)|
 | Hands-On Machine Learning with R 							| [website](https://bradleyboehmke.github.io/HOML/)|
 | Zero to Mastery TensorFlow for Deep Learning Book			| [dev.mrdbourke.com/](https://dev.mrdbourke.com/tensorflow-deep-learning/)|
 | Introduction to Probability for Data Science					| [probability4datascience](https://probability4datascience.com/)|
 | Graph Representation Learning Book							| [cs.mcgill.ca](https://www.cs.mcgill.ca/~wlh/grl_book/)|
 | Interpretable Machine Learning								| [christophm](https://christophm.github.io/interpretable-ml-book/)|
 | Computer Vision: Algorithms and Applications, 2nd ed.		| [szeliski.org](https://szeliski.org/Book/)

 
 
 
## ML in Production


 | Title and Source                                             | Link                               				         |
 |------------------------------------------------------------  | -----------------------------------------------------------|
 | 	Introduction to Docker       	 							| [Docker](https://carpentries-incubator.github.io/docker-introduction/)|
 |  MLOps Basics												| [GitHub](https://github.com/graviraja/MLOps-Basics)| 
  

## Quantum ML

 | Title and Source                                             | Link                               				         |
 |------------------------------------------------------------  | -----------------------------------------------------------|
 | 	Quantum machine learning      	 							| [pennylane.ai](https://pennylane.ai/qml/)|


## DataSets

 | Title and Source                                             | Link                               				         |
 |------------------------------------------------------------  | -----------------------------------------------------------|
 | Yelp Open Dataset											| [yelp](https://www.yelp.com/dataset)						 | 
 | Machine Translation 											| [website](https://www.manythings.org/anki/)				 |
 | IndicNLP Corpora (Indian languages)							| [ai4bharat](https://indicnlp.ai4bharat.org/explorer/)		 |
 | Amazon product co-purchasing network metadata				| [snap.stanford.edu/](https://snap.stanford.edu/data/amazon-meta.html)|
 | Stanford Question Answering Dataset (SQuAD)					| [website](https://rajpurkar.github.io/SQuAD-explorer/)
  
 
## Other Useful Websites


1.	[Papers with Code](https://paperswithcode.com/sota)
2.	[Two Minute Papers - Youtube](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
3.  [The Missing Semester of Your CS Education](https://missing.csail.mit.edu/2020/)
4.  [Workera :  Measure data-AI skills](https://workera.ai/)
5.  [Machine learning mastery](https://machinelearningmastery.com/start-here/)
6.  [From Data to viz: Guide for your graph](https://www.data-to-viz.com/)
7.  [datatalks club](https://datatalks.club/)
8.  [Machine Learning for Art](https://ml4a.net/fundamentals/)
10. [applyingml](https://applyingml.com/)
11. [Deep Learning Drizzle](https://deep-learning-drizzle.github.io/index.html#opt4ml)
12. [The Machine & Deep Learning Compendium](https://book.mlcompendium.com/)
13. [connectedpapers - Research Papers](https://www.connectedpapers.com/)
14. [Papers and Latest Research - deepai](https://deepai.org/)
15. [Tracking Progress in NLP](https://nlpprogress.com/)
16. [NLP Blogs by Sebastian Ruder](https://ruder.io/)
17. [labmlai for papers](https://papers.labml.ai/)

## Other Useful GitRepo

1. [Applied-ml - Papers and blogs by organizations ](https://github.com/eugeneyan/applied-ml)
2. [List Machine learning Python libraries](https://github.com/ml-tooling/best-of-ml-python)
3. [ML From Scratch - Implementations of models/algorithms](https://github.com/eriklindernoren/ML-From-Scratch)
4. [What the f*ck Python?](https://github.com/satwikkansal/wtfpython)
5. [scikit-learn user guide: step-step approach](https://scikit-learn.org/stable/user_guide.html)
6. [NLP Tutorial Code with DL](https://github.com/graykode/nlp-tutorial)
7. [awesome-mlops](https://github.com/visenger/awesome-mlops)
8. [Text Classification Algorithms: A Survey](https://github.com/kk7nc/Text_Classification)
9. [ML use cases by company](https://github.com/khangich/machine-learning-interview/blob/master/appliedml.md)

## Blogs and Webinar
1. [Recommendation algorithms and System design](https://www.theinsaneapp.com/2021/03/system-design-and-recommendation-algorithms.html?m=1)
2. [Machine Learning System Design](https://becominghuman.ai/machine-learning-system-design-f2f4018f2f8?gi=942874b21d0e)
3. [Lil'BLog](https://lilianweng.github.io/lil-log/)


## Must Read Research Paper

 **NLP [Text]** 

1. [Text Classification Algorithms: A Survey](https://arxiv.org/abs/1904.08067)
2. [Deep Learning Based Text Classification: A Comprehensive Review](https://arxiv.org/abs/2004.03705)
3. [Compression of Deep Learning Models for Text: A Survey](https://arxiv.org/abs/2008.05221)
4. [A Survey on Text Classification: From Shallow to Deep Learning](https://arxiv.org/pdf/2008.00364.pdf)
4. [A Survey of Transformers](https://arxiv.org/abs/2106.04554)
5. [AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing](https://arxiv.org/abs/2108.05542)
6. [Graph Neural Networks for Natural Language Processing: A Survey](https://arxiv.org/abs/2106.06090)
8. [A Survey of Data Augmentation Approaches for NLP](https://arxiv.org/abs/2105.03075)
9. [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://aclanthology.org/2021.naacl-main.201.pdf)
10. [Evaluation of Text Generation: A Survey](https://arxiv.org/pdf/2006.14799.pdf) 
11. [A Survey of Transfer learning In NLP](https://arxiv.org/pdf/2007.04239.pdf)
12. [A Systematic Survey of Prompting Methods in NLP](https://arxiv.org/pdf/2107.13586.pdf)

**OCR [Optical Character Recognition]** 

1. [Survey of Post-OCR Processing Approaches](https://dl.acm.org/doi/pdf/10.1145/3453476)

## Company Tech Blogs 

1. [AssemblyAI](https://www.assemblyai.com/blog)
2. [Grammarly](https://www.grammarly.com/blog/engineering/)
3. [Huggingface](https://huggingface.co/blog)
4. [Uber](https://eng.uber.com/category/articles/ai/)
5. [Netflix](https://netflixtechblog.com/)
6. [Spotify Research](https://research.atspotify.com/blog/) | [Engineering](https://engineering.atspotify.com/)



