{"id":13737296,"url":"https://github.com/d0r1h/ML-University","last_synced_at":"2025-05-08T13:33:46.225Z","repository":{"id":37917842,"uuid":"291299282","full_name":"d0r1h/ML-University","owner":"d0r1h","description":"Machine Learning Open Source University","archived":false,"fork":false,"pushed_at":"2025-01-13T13:04:02.000Z","size":864,"stargazers_count":871,"open_issues_count":0,"forks_count":110,"subscribers_count":36,"default_branch":"master","last_synced_at":"2025-01-13T13:51:17.906Z","etag":null,"topics":["artificial-intelligence","awsome","awsome-list","computer-science","course","data-science","deep-learning","free","learning","machine-learning","mathematics","natural-language-processing","neural-network","open-source","reinforcement-learning","university"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/d0r1h.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2020-08-29T15:40:50.000Z","updated_at":"2025-01-13T13:04:05.000Z","dependencies_parsed_at":"2024-01-11T14:13:49.498Z","dependency_job_id":"db773ed6-99d8-4ee2-b472-32b9952a3b7a","html_url":"https://github.com/d0r1h/ML-University","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/d0r1h%2FML-University","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/d0r1h%2FML-University/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/d0r1h%2FML-University/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/d0r1h%2FML-University/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/d0r1h","download_url":"https://codeload.github.com/d0r1h/ML-University/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253077653,"owners_count":21850361,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","awsome","awsome-list","computer-science","course","data-science","deep-learning","free","learning","machine-learning","mathematics","natural-language-processing","neural-network","open-source","reinforcement-learning","university"],"created_at":"2024-08-03T03:01:40.213Z","updated_at":"2025-05-08T13:33:46.211Z","avatar_url":"https://github.com/d0r1h.png","language":null,"funding_links":[],"categories":["Others"],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n    \u003cbr\u003e\n    \u003cimg src=\"https://github.com/d0r1h/ML-University/blob/master/ml_logo.png\" width=\"300\"/\u003e\n    \u003cbr\u003e\n\u003cp\u003e\n\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://hits.seeyoufarm.com\"\u003e\u003cimg src=\"https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Fd0r1h%2FML-University\u0026count_bg=%2379C83D\u0026title_bg=%23555555\u0026icon=\u0026icon_color=%23E7E7E7\u0026title=hits\u0026edge_flat=false\"/\u003e\u003c/a\u003e\n \u003ca href=\"https://twitter.com/intent/tweet?text=Checkout this awesome Machine Learning University Repo on Github text:\u0026url=https%3A%2F%2Fgithub.com%2Fd0r1h%2FML-University\"\u003e\u003cimg alt=\"tweet\" src=\"https://img.shields.io/twitter/url?style=social\u0026url=https%3A%2F%2Fgithub.com%2Fd0r1h%2FML-University\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\u003ch3 align=\"center\"\u003e\n    \u003cp\u003eA Free Machine Learning University \u003c/p\u003e\n\u003c/h3\u003e\n\u003cbr\u003e\n\nMachine Learning Open Source University is an IDEA of free-learning of a ML enthusiast for all other ML enthusiast\n\n**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.\n\n\n**Table of Contents**\n\n1. [Getting Started](#getting-started)\n2. [Mathematics](#mathematics)\n3. [Machine Learning](#machine-learning)\n4. [Deep Learning](#deep-learning)\n5. [Natural language processing](#natural-language-processing)\n6. [Reinforcement learning](#reinforcement-learning)\n7. [LLM](LLM (Large Language Model))\n8. [Books](#books)\n9. [ML in Production](#ml-in-production)\n10. [Quantum ML](#quantum-ml)\n11. [DataSets](#datasets)\n12. [Other Useful Websites](#other-useful-websites)\n13. [Other Useful GitRrpo](#other-useful-gitrepo)\n14. [Blogs and Webinar](#blogs-and-webinar) \n15. [Must Read Research Paper](#must-read-research-paper)\n16. [Company Tech Blogs](#company-tech-blogs)\n\n\n\n\n\n\n\n\n\n## Getting Started\n\n | Title and Source                                             | Link                               \t\t\t\t          |\n |------------------------------------------------------------  | -------------------------------------------------------------|\n | Elements of AI :  Part-1                                     | [WebSite](https://course.elementsofai.com/)\t\t\t\t  |\n | Elements of AI :  Part-2                                     | [WebSite](https://buildingai.elementsofai.com/) \t\t\t  |\n | CS50’s Introduction to AI\t**Harvard**\t\t\t            | [Cs50 WebSite](https://cs50.harvard.edu/ai/2020/)\t\t\t  |\n | 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/)\n | Practical Data Ethics\t\t\t\t\t\t\t\t\t\t| [fast.ai](https://ethics.fast.ai/)\n | Machine learning Mastery Getting Started \t\t\t\t\t| [machinelearningmastery](https://machinelearningmastery.com/start-here/)\n | Design and Analysis of Algorithms **MIT**\t\t\t\t\t| [ocw.mit.edu](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2015/)\n | AI: Principles and Techniques **Stanford** \t\t\t\t\t| [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rO1NB9TD4iUZ3qghGEGtqNX)|\n | The Private AI Series \t\t\t\t\t\t\t\t\t\t| [openmined](https://courses.openmined.org/courses)|\n\n \n\n## Mathematics\n\n\n | Title and Source                                             | Link                               \t\t\t\t          |\n |------------------------------------------------------------  | -------------------------------------------------------------\n | Statistics in Machine Learning (Krish Naik)                  | [YouTube](https://www.youtube.com/playlist?list=PLZoTAELRMXVMhVyr3Ri9IQ-t5QPBtxzJO)\n | Computational Linear Algebra for Coders\t\t\t\t\t\t| [fast.ai](https://github.com/fastai/numerical-linear-algebra/blob/master/README.md)\n | Linear Algebra  **MIT**\t\t\t\t\t\t\t\t\t\t| [WebSite](https://openlearninglibrary.mit.edu/courses/course-v1:OCW+18.06SC+2T2019/course/)|\n | Statistics by zstatistics\t\t\t\t\t\t\t\t\t| [WebSite](https://www.zstatistics.com/videos)|\n | Essence of linear algebra by 3Blue1Brown\t\t\t\t\t\t| [YouTube](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)|\n | SEEING THEORY (Visual Probability)\t**brown**    \t\t    | [WebSite](https://seeing-theory.brown.edu/basic-probability/index.html)|\n | 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/)\n | Math for Machine Learning \t\t\t\t\t\t\t\t\t| [YouTube](https://www.youtube.com/playlist?app=desktop\u0026list=PLD80i8An1OEGZ2tYimemzwC3xqkU0jKUg) |\n | Statistics for Applications **MIT** | [YouTube](https://www.youtube.com/playlist?list=PLUl4u3cNGP60uVBMaoNERc6knT_MgPKS0) \n | Introduction to Mathematical Thinking | [Website](http://imt-decal.org/)|\n\n\n\n## Machine Learning\n\n | Title and Source                                             | Link                               \t\t\t\t           |\n |------------------------------------------------------------  | -------------------------------------------------------------|\n | Introduction to Machine Learning with scikit-learn \t\t\t| [dataschool](https://courses.dataschool.io/introduction-to-machine-learning-with-scikit-learn)|\n | Introduction to Machine Learning\t\t\t\t\t\t\t\t| [sebastianraschka](https://sebastianraschka.com/blog/2021/ml-course.html)\n | Open Machine Learning Course \t\t\t\t\t\t\t\t| [mlcourse.ai](https://mlcourse.ai/)\t\t\t\t\t\t   |\n | Machine Learning (CS229) **Stanford**\t\t\t\t\t\t| [WebSite](http://cs229.stanford.edu/syllabus-spring2020.html) [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)|\n | Introduction to Machine Learning **MIT** \t\t\t\t\t| [WebSite](https://tinyurl.com/ybl6udcr)\t\t\t\t\t   |\n | Machine Learning Systems Design 2021 (CS329S) **Stanford**   | [WebSite](https://stanford-cs329s.github.io/syllabus.html)   |\n | Applied Machine Learning 2020 (CS5787) **Cornell Tech**      | [YouTube](https://www.youtube.com/playlist?list=PL2UML_KCiC0UlY7iCQDSiGDMovaupqc83)\n | Machine Learning for Healthcare **MIT** \t\t\t\t\t\t| [WebSite](https://tinyurl.com/yxgeesdf)\t\t\t\t\t   |\n | Machine Learning for Trading **Georgia Tech**\t\t\t\t| [WebSite](https://lucylabs.gatech.edu/ml4t/)\t\t\t\t   |\t\n | Introduction to Machine Learning for Coders\t\t\t\t\t| [fast.ai](https://course18.fast.ai/ml.html)\n | Machine Learning Crash Course\t\t\t\t\t\t\t\t| [Google AI](https://developers.google.com/machine-learning/crash-course)|\n | Machine Learning with Python \t\t\t\t\t\t\t\t| [freecodecamp](https://www.freecodecamp.org/learn/machine-learning-with-python/)|\n | Deep Reinforcement Learning:CS285 **UC Berkeley**\t\t\t| [YouTube](https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc)|\n | Probabilistic Machine Learning **University of Tübingen**    | [YouTube](https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd)|\n | Machine Learning with Graphs(CS224W) **Stanford** \t\t\t| [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn)|\n | Machine Learning in Production **CMU**\t\t\t\t\t\t| [WebSite](https://ckaestne.github.io/seai/)|\n | Machine Learning \u0026 Deep Learning Fundamentals                | [deeplizard](https://deeplizard.com/learn/video/gZmobeGL0Yg)|\n | Interpretability and Explainability in Machine Learning      | [WebSite](https://interpretable-ml-class.github.io/)|\n | Practical Machine Learning 2021 **Stanford**\t\t\t\t\t| [WebSite](https://c.d2l.ai/stanford-cs329p/index.html#)|\n | Machine Learning **VU University** \t\t\t\t\t\t\t| [WebSite](https://mlvu.github.io/)|\n | Machine Learning for Cyber Security **Purdue University**    | [YouTube](https://www.youtube.com/playlist?list=PL74sw1ohGx7GHqDHCkXZeqMQBVUTMrVLE)|\n | Audio Signal Processing for Machine Learning \t\t\t\t| [YouTube](https://www.youtube.com/playlist?list=PL-wATfeyAMNqIee7cH3q1bh4QJFAaeNv0)|\n | Machine learning \u0026 causal inference **Stanford**\t\t\t\t| [YouTube](https://www.youtube.com/playlist?list=PLxq_lXOUlvQAoWZEqhRqHNezS30lI49G-)|\n | Machine learning cs156 **caltech**                           | [YouTube](https://www.youtube.com/playlist?list=PLD63A284B7615313A) |\n | 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) | \n | Advanced Topics in Machine Learning **Caltech**              | [WebSite](https://1five9.github.io/)\n\n \t\n\n## Deep Learning\n \n \n | Title and Source                                             | Link                               \t\t\t\t           |\n |------------------------------------------------------------  | -------------------------------------------------------------|\n | Introduction to Deep Learning(6.S191) **MIT**\t\t \t\t| [YouTube](https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI)\t\t\t\t\t   |\n | Introduction to Deep Learning\t\t\t\t\t\t\t\t| [sebastianraschka](https://sebastianraschka.com/blog/2021/dl-course.html)\n | Deep Learning **NYU**\t\t\t\t\t \t\t\t\t\t| [WebSite](https://atcold.github.io/pytorch-Deep-Learning/)  [2021](https://atcold.github.io/NYU-DLSP21/) |\n | Deep Learning (CS182) **UC Berkeley**\t\t\t\t\t\t| [YouTube](https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A)\n | Deep Learning Lecture Series\t**DeepMind x UCL**\t\t\t    | [YouTube](https://www.youtube.com/playlist?list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF)|\n | Deep Learning (CS230) **Stanford**\t\t\t\t\t\t    | [WebSite](https://cs230.stanford.edu/lecture/)               | \n | CNN for Visual Recognition(CS231n) **Stanford**    \t\t    | [WebSite-2020](https://cs231n.github.io/)  [YouTube-2017](https://tinyurl.com/y2gghbvs)|\n | Full Stack Deep Learning   \t\t\t\t\t\t\t\t\t| [WebSite](https://course.fullstackdeeplearning.com/)[2021](https://fullstackdeeplearning.com/spring2021/)|\n | Practical Deep Learning for Coders, v3                       | [fast.ai](https://course19.fast.ai/index.html)\t\t\t   |\n | Deep Learning Crash Course 2021 d2l.ai \t\t\t\t\t\t| [YouTube](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQsDaBNtcFwMQuJw_djFnbd)|\n | Deep Learning for Computer Vision **Michigan**\t\t\t\t| [WebSite](https://web.eecs.umich.edu/~justincj/teaching/eecs498/FA2020/)|\n | Neural Networks from Scratch in Python by Sentdex\t\t\t| [YouTube](https://www.youtube.com/playlist?app=desktop\u0026list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3)|\n | Keras - Python Deep Learning Neural Network API\t\t\t\t| [deeplizard](https://deeplizard.com/learn/video/RznKVRTFkBY)|\n | Reproducible Deep Learning\t\t\t\t\t\t\t\t\t| [sscardapane.it](https://www.sscardapane.it/teaching/reproducibledl/)|\n | PyTorch Fundamentals \t\t\t\t\t\t\t\t\t\t| [microsoft](https://docs.microsoft.com/en-us/learn/paths/pytorch-fundamentals/)|\n | Geometric Deep Learing (GDL100)\t\t\t\t\t\t\t\t| [geometricdeeplearning](https://geometricdeeplearning.com/lectures/)|\n | Deep learning Neuromatch Academy \t\t\t\t\t\t\t| [neuromatch](https://deeplearning.neuromatch.io/tutorials/intro.html)\n | Deep Learning for Molecules and Materials\t\t\t\t\t| [WebSite](https://whitead.github.io/dmol-book/intro.html)|\n | Deep Learning course for Vision\t\t\t\t\t\t\t\t| [arthurdouillard.com](https://arthurdouillard.com/deepcourse/)|\n | Deep Multi-Task and Meta Learning (CS330) **Stanford**  \t\t| [WebSite](https://cs330.stanford.edu/) [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5)|\n | Deep Learning Interviews book \t\t\t\t\t\t\t\t| [WebSite](https://github.com/BoltzmannEntropy/interviews.ai)|\n | Deep Learning for Computer Vision 2021                       | [YouTube](https://www.youtube.com/playlist?list=PL_Z2_U9MIJdNgFM7-f2fZ9ZxjVRP_jhJv)\n | Deep Learning 2022 **CMU**                                   | [YouTube](https://www.youtube.com/playlist?list=PLp-0K3kfddPxRmjgjm0P1WT6H-gTqE8j9)   \n | UvA Deep Learning                                            | [WebSite](https://uvadlc.github.io/)\n\n\n## Natural language processing \n\n | Title and Source                                             | Link                               \t\t\t\t  \t\t   |\n | ------------------------------------------------------------ | -----------------------------------------------------------|\n | Natural Language Processing AWS\t\t\t\t\t\t\t\t| [YouTube](https://www.youtube.com/playlist?list=PL8P_Z6C4GcuWfAq8Pt6PBYlck4OprHXsw)\n | NLP - Krish Naik \t\t\t\t                            | [YouTube](https://www.youtube.com/playlist?list=PLZoTAELRMXVMdJ5sqbCK2LiM0HhQVWNzm)\n | NLP with Deep Learning(CS224N) 2019 **Stanford**     \t\t| [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z) [2021](https://www.youtube.com/playlist?list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ)\n | A Code-First Introduction to Natural Language Processing     | [fast.ai](https://www.fast.ai/2019/07/08/fastai-nlp/)|\n | CMU Neural Nets for NLP 2021  **Carnegie Mellon University** | [YouTube](https://www.youtube.com/playlist?list=PL8PYTP1V4I8AkaHEJ7lOOrlex-pcxS-XV)|\n | Speech and Language Processing **Stanford** \t\t\t\t\t| [WebSite](https://web.stanford.edu/~jurafsky/slp3/) |\n | Natural Language Understanding (CS224U) **Stanford**\t\t\t| [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rObpMCir6rNNUlFAn56Js20) [2022](https://web.stanford.edu/class/cs224u/)\n | NLP with Dan Jurafsky and Chris Manning, 2012 **Stanford**   | [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rOFZnDyrlW3-nI7tMLtmiJZ)|\n | Intro to NLP with spaCy   \t\t\t\t\t\t\t\t\t| [YouTube](https://www.youtube.com/playlist?list=PLBmcuObd5An559HbDr_alBnwVsGq-7uTF)|\n | Advanced NLP with spaCy \t\t\t\t\t\t\t\t\t\t| [website](https://course.spacy.io/en/)                                             |\n | Applied Language Technology \t\t\t\t\t\t\t\t\t| [website](https://applied-language-technology.readthedocs.io/en/latest/)|\n | Advanced Natural Language Processing **Umass**\t\t\t\t| [website](https://people.cs.umass.edu/~miyyer/cs685/schedule.html) [YouTube 2020](https://www.youtube.com/playlist?list=PLWnsVgP6CzadmQX6qevbar3_vDBioWHJL)|\n | Huggingface Course\t\t\t\t\t\t\t\t\t\t\t| [huggingface.co](https://huggingface.co/course/chapter1?fw=tf)|\n | NLP Course **Michigan**\t\t\t\t\t\t\t\t\t\t| [github](https://github.com/deskool/nlp-class)|\n | Multilingual NLP 2020 **CMU**\t\t\t\t\t\t\t\t| [YouTube](https://www.youtube.com/playlist?list=PL8PYTP1V4I8CHhppU6n1Q9-04m96D9gt5)|\n | Advanced NLP 2021 **CMU**\t\t\t\t\t\t\t\t\t| [YouTube](https://www.youtube.com/playlist?list=PL8PYTP1V4I8AYSXn_GKVgwXVluCT9chJ6)|\n | Transformers United **stanford**                             | [Website](https://web.stanford.edu/class/cs25/)  [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM) |  \n | CS324 Large Language Models | [Website](https://stanford-cs324.github.io/winter2022/)|\n\n  \n\n## Reinforcement learning\n\n | Title and Source                                             | Link\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                         |\n |------------------------------------------------------------  | -----------------------------------------------------------|\n | Reinforcement Learning(CS234)  **Stanford** \t\t\t\t\t| [YouTube-2019](https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u)|\n | Introduction to reinforcement learning **DeepMind**\t\t\t| [YouTube-2015](https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ)|\n | Reinforcement Learning Course  **DeepMind \u0026 UCL**\t\t\t| [YouTube-2018](https://www.youtube.com/playlist?list=PLqYmG7hTraZBKeNJ-JE_eyJHZ7XgBoAyb)|\n | Advanced Deep Learning \u0026 Reinforcement Learning        \t\t| [YouTube](https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs)|\n | DeepMind x UCL Reinforcement Learning 2021\t\t\t\t\t| [YouTube](https://www.youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm)\n \n\n\n## LLM (Large Language Model)\n\n| Title and Source                                             | Link |\n |------------------------------------------------------------  | -----------------------------------------------------------|\n| Large Language Model Systems | [Website](https://llmsystem.github.io/llmsystem2025spring/) |\n\n \n## Books\n\n\n | Title and Source                                             | Link                               \t\t\t\t         |\n |------------------------------------------------------------  | -----------------------------------------------------------|\n | Scientific Python Lectures\t\t \t\t\t\t\t\t\t| [ScipyLectures](https://scipy-lectures.org/_downloads/ScipyLectures-simple.pdf)|\n | Mathematics for Machine Learning\t\t\t\t\t\t\t    | [mml-book](https://mml-book.github.io/book/mml-book.pdf)\t |\n | An Introduction to Statistical Learning                      | [statlearning](https://www.statlearning.com/)              |\n | Think Stats \t\t\t\t\t\t\t\t\t\t\t\t\t| [Think Stats](https://greenteapress.com/wp/think-stats-2e/)|\n | Python Data Science Handbook                                 | [Python For DS](https://jakevdp.github.io/PythonDataScienceHandbook/)|\n | Natural Language Processing with Python - NLTK               | [NLTK](https://www.nltk.org/book/)\t\t\t\t\t\t |\n | Deep Learning by Ian Goodfellow             \t\t\t\t\t| [deeplearningbook](https://www.deeplearningbook.org/)\t\t |\n | Dive into Deep Learning \t\t\t\t\t\t\t\t\t\t| [d2l.ai](https://d2l.ai/index.html)\n | Approaching (Almost) Any Machine Learning Problem    \t\t| [AAANLP](https://github.com/abhishekkrthakur/approachingalmost/blob/master/AAAMLP.pdf)|\n | Neural networks and Deep learning\t\t\t\t\t\t\t| [neuralnetworksanddeeplearning](http://neuralnetworksanddeeplearning.com/index.html)|\n | AutoML: Methods, Systems, Challenges (first book on AutoML)  | [automl](https://www.automl.org/book/)|\n | Feature Engineering and Selection\t \t\t\t\t\t\t| [bookdown.org](https://bookdown.org/max/FES/)|\n | Introduction to Machine Learning Interviews Book\t\t\t\t| [huyenchip.com](https://huyenchip.com/ml-interviews-book/)|\n | Hands-On Machine Learning with R \t\t\t\t\t\t\t| [website](https://bradleyboehmke.github.io/HOML/)|\n | Zero to Mastery TensorFlow for Deep Learning Book\t\t\t| [dev.mrdbourke.com/](https://dev.mrdbourke.com/tensorflow-deep-learning/)|\n | Introduction to Probability for Data Science\t\t\t\t\t| [probability4datascience](https://probability4datascience.com/)|\n | Graph Representation Learning Book\t\t\t\t\t\t\t| [cs.mcgill.ca](https://www.cs.mcgill.ca/~wlh/grl_book/)|\n | Interpretable Machine Learning\t\t\t\t\t\t\t\t| [christophm](https://christophm.github.io/interpretable-ml-book/)|\n | Computer Vision: Algorithms and Applications, 2nd ed.\t\t| [szeliski.org](https://szeliski.org/Book/)\n\n \n \n \n## ML in Production\n\n\n | Title and Source                                             | Link                               \t\t\t\t         |\n |------------------------------------------------------------  | -----------------------------------------------------------|\n | \tIntroduction to Docker       \t \t\t\t\t\t\t\t| [Docker](https://carpentries-incubator.github.io/docker-introduction/)|\n |  MLOps Basics\t\t\t\t\t\t\t\t\t\t\t\t| [GitHub](https://github.com/graviraja/MLOps-Basics)| \n |  Effective MLOps: Model Development                           | [wandb](https://www.wandb.courses/courses/effective-mlops-model-development/)|\n  \n\n## Quantum ML\n\n | Title and Source                                             | Link                               \t\t\t\t         |\n |------------------------------------------------------------  | -----------------------------------------------------------|\n | \tQuantum machine learning      \t \t\t\t\t\t\t\t| [pennylane.ai](https://pennylane.ai/qml/)|\n\n\n## DataSets\n\n | Title and Source                                             | Link                               \t\t\t\t         |\n |------------------------------------------------------------  | -----------------------------------------------------------|\n | Yelp Open Dataset\t\t\t\t\t\t\t\t\t\t\t| [yelp](https://www.yelp.com/dataset)\t\t\t\t\t\t | \n | Machine Translation \t\t\t\t\t\t\t\t\t\t\t| [website](https://www.manythings.org/anki/)\t\t\t\t |\n | IndicNLP Corpora (Indian languages)\t\t\t\t\t\t\t| [ai4bharat](https://indicnlp.ai4bharat.org/explorer/)\t\t |\n | Amazon product co-purchasing network metadata\t\t\t\t| [snap.stanford.edu/](https://snap.stanford.edu/data/amazon-meta.html)|\n | Stanford Question Answering Dataset (SQuAD)\t\t\t\t\t| [website](https://rajpurkar.github.io/SQuAD-explorer/)\n  \n \n## Other Useful Websites\n\n\n1.\t[Papers with Code](https://paperswithcode.com/sota)\n2.\t[Two Minute Papers - Youtube](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)\n3.  [The Missing Semester of Your CS Education](https://missing.csail.mit.edu/2020/)\n4.  [Workera :  Measure data-AI skills](https://workera.ai/)\n5.  [Machine learning mastery](https://machinelearningmastery.com/start-here/)\n6.  [From Data to viz: Guide for your graph](https://www.data-to-viz.com/)\n7.  [datatalks club](https://datatalks.club/)\n8.  [Machine Learning for Art](https://ml4a.net/fundamentals/)\n10. [applyingml](https://applyingml.com/)\n11. [Deep Learning Drizzle](https://deep-learning-drizzle.github.io/index.html#opt4ml)\n12. [The Machine \u0026 Deep Learning Compendium](https://book.mlcompendium.com/)\n13. [connectedpapers - Research Papers](https://www.connectedpapers.com/)\n14. [Papers and Latest Research - deepai](https://deepai.org/)\n15. [Tracking Progress in NLP](https://nlpprogress.com/)\n16. [NLP Blogs by Sebastian Ruder](https://www.ruder.io/)\n17. [labmlai for papers](https://papers.labml.ai/)\n\n## Other Useful GitRepo\n\n1. [Applied-ml - Papers and blogs by organizations ](https://github.com/eugeneyan/applied-ml)\n2. [List Machine learning Python libraries](https://github.com/ml-tooling/best-of-ml-python)\n3. [ML From Scratch - Implementations of models/algorithms](https://github.com/eriklindernoren/ML-From-Scratch)\n4. [What the f*ck Python?](https://github.com/satwikkansal/wtfpython)\n5. [scikit-learn user guide: step-step approach](https://scikit-learn.org/stable/user_guide.html)\n6. [NLP Tutorial Code with DL](https://github.com/graykode/nlp-tutorial)\n7. [awesome-mlops](https://github.com/visenger/awesome-mlops)\n8. [Text Classification Algorithms: A Survey](https://github.com/kk7nc/Text_Classification)\n9. [ML use cases by company](https://github.com/khangich/machine-learning-interview/blob/master/appliedml.md)\n\n## Blogs and Webinar\n1. [Recommendation algorithms and System design](https://www.theinsaneapp.com/2021/03/system-design-and-recommendation-algorithms.html)\n2. [Machine Learning System Design](https://becominghuman.ai/machine-learning-system-design-f2f4018f2f8?gi=942874b21d0e)\n3. [Lil'BLog](https://lilianweng.github.io/lil-log/)\n\n\n## Must Read Research Paper\n\n **NLP [Text]** \n\n1. [Text Classification Algorithms: A Survey](https://arxiv.org/abs/1904.08067)\n2. [Deep Learning Based Text Classification: A Comprehensive Review](https://arxiv.org/abs/2004.03705)\n3. [Compression of Deep Learning Models for Text: A Survey](https://arxiv.org/abs/2008.05221)\n4. [A Survey on Text Classification: From Shallow to Deep Learning](https://arxiv.org/pdf/2008.00364.pdf)\n4. [A Survey of Transformers](https://arxiv.org/abs/2106.04554)\n5. [AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing](https://arxiv.org/abs/2108.05542)\n6. [Graph Neural Networks for Natural Language Processing: A Survey](https://arxiv.org/abs/2106.06090)\n8. [A Survey of Data Augmentation Approaches for NLP](https://arxiv.org/abs/2105.03075)\n9. [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://aclanthology.org/2021.naacl-main.201.pdf)\n10. [Evaluation of Text Generation: A Survey](https://arxiv.org/pdf/2006.14799.pdf) \n11. [A Survey of Transfer learning In NLP](https://arxiv.org/pdf/2007.04239.pdf)\n12. [A Systematic Survey of Prompting Methods in NLP](https://arxiv.org/pdf/2107.13586.pdf)\n\n**OCR [Optical Character Recognition]** \n\n1. [Survey of Post-OCR Processing Approaches](https://dl.acm.org/doi/pdf/10.1145/3453476)\n\n## Company Tech Blogs \n\n1. [AssemblyAI](https://www.assemblyai.com/blog)\n2. [Grammarly](https://www.grammarly.com/blog/engineering/)\n3. [Huggingface](https://huggingface.co/blog)\n4. [Uber](https://eng.uber.com/category/articles/ai/)\n5. [Netflix](https://netflixtechblog.com/)\n6. [Spotify Research](https://research.atspotify.com/blog/) | [Engineering](https://engineering.atspotify.com/)\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fd0r1h%2FML-University","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fd0r1h%2FML-University","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fd0r1h%2FML-University/lists"}