{"id":13530704,"url":"https://github.com/rueedlinger/ml-resources","last_synced_at":"2025-04-01T19:30:34.047Z","repository":{"id":77508048,"uuid":"339658683","full_name":"rueedlinger/ml-resources","owner":"rueedlinger","description":"A curated list of statistics, data visualization and machine learning resources which in find useful, have read or want to read.","archived":false,"fork":false,"pushed_at":"2023-12-02T14:55:48.000Z","size":31,"stargazers_count":8,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2024-11-02T17:36:27.762Z","etag":null,"topics":["curated-list","data-science","data-visualization","deep-learning","machine-learning","statistics"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rueedlinger.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-02-17T08:37:31.000Z","updated_at":"2024-08-25T13:52:18.000Z","dependencies_parsed_at":null,"dependency_job_id":"c5ac0a4d-04ad-4940-a681-35c83d8c5b4e","html_url":"https://github.com/rueedlinger/ml-resources","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/rueedlinger%2Fml-resources","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rueedlinger%2Fml-resources/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rueedlinger%2Fml-resources/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rueedlinger%2Fml-resources/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rueedlinger","download_url":"https://codeload.github.com/rueedlinger/ml-resources/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246700018,"owners_count":20819812,"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":["curated-list","data-science","data-visualization","deep-learning","machine-learning","statistics"],"created_at":"2024-08-01T07:00:53.812Z","updated_at":"2025-04-01T19:30:33.778Z","avatar_url":"https://github.com/rueedlinger.png","language":null,"readme":"# Curated List of Machine Learning Resources\n\nA curated list of machine Learning resource I find very useful. The List is structured into the following topics:\n\n- **Programming** - resources to get started with the most used programming languages in machine learning.\n- **Mathematics** - great resource to get the mathematical foundation you need for machine learning.\n- **Statistics** - the statistics basic you should know.\n- **Machine Learning** - a lot of interesting resources with focus on machine learning, deep learning and data science.\n- **Data Visualization** - collection of resource which teaches you to visualize your results.\n\n# Books\n\n## Programming\n\n- [Think Python - How to Think Like a Computer Scientist](https://greenteapress.com/wp/think-python/), 2012, by Allen B. Downey.\n\n- [R for Data Science](https://r4ds.had.co.nz/), 2017, by Hadley Wickham and Garrett Grolemund.\n\n## Mathematics\n\n- [A Mathematics Course for Political and Social Research](https://people.duke.edu/~das76/MooSieBook.html), 2013, by Will H. Moore, David A. Siegel.\n- [Mathematics for Machine Learning](https://mml-book.github.io/), 2020, by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong.\n- [Linear Algebra for Machine Learning: Complete Math Course on YouTube](https://www.jonkrohn.com/posts/2021/5/9/linear-algebra-for-machine-learning-complete-math-course-on-youtube), 2021, by Jon Krohn\n\n## Statistics\n\n- [An Introduction to Statistical Learning](https://www.statlearning.com/), 2013, by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.\n- [The Elements of Statistical Learning](https://web.stanford.edu/~hastie/ElemStatLearn/), 2009, 2nd edition, by Trevor Hastie Robert Tibshirani, Jerome Friedman.\n- [Think Stats - Exploratory Data Analysis in Python](https://greenteapress.com/wp/think-stats-2e/), 2014, 2nd edition, by Allen B. Downey.\n- [Think Bayes - Bayesian Statistics Made Simple](https://greenteapress.com/wp/think-bayes/), 2012, by Allen B. Downey.\n\n## Machine Learning\n\n- [Probabilistic Machine Learning - An Introduction](https://probml.github.io/pml-book/book1.html), 2021, by Kevin Patrick Murphy.\n- [Introduction to Data Science - Data Analysis and Prediction Algorithms with R](https://rafalab.github.io/dsbook/), 2021, by Rafael A. Irizarry.\n- [Deep Learning](https://www.deeplearningbook.org/), 2016, by Ian Goodfellow and Yoshua Bengio and Aaron Courville.\n- [Dive into Deep Learning](https://d2l.ai/), 2020, by Aston Zhang and Zachary C. Lipton and Mu Li and Alexander J. Smola\n- [Deep Learning for Coders with Fastai and PyTorch](https://course.fast.ai/), 2020, by Sylvain Gugger, Jeremy Howard.\n- [Automated Machine Learning: Methods, Systems, Challenges}](https://www.automl.org/book/), 2018, by Hutter, Frank and Kotthoff, Lars and Vanschoren, Joaquin\n- [Interpretable Machine Learning - A Guide for Making Black Box Models Explainable.](https://christophm.github.io/interpretable-ml-book/), 2020, by Christoph Molnar\n- [Speech and Language Processing -  An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition](https://web.stanford.edu/~jurafsky/slp3/), 2023, 3rd ed. draft, by Dan Jurafsky and James H. Martin \n\n## Data Visualization\n\n- [Fundamentals of Data Visualization](https://clauswilke.com/dataviz/), 2019, by Claus O. Wilke.\n\n# Online Course (MOOC)\n\n## Machine Learning\n\n- [Learning from Data](https://work.caltech.edu/telecourse), introductory machine learning online course by Yaser S. Abu-Mostafa from Caltech. \n\n- [CS229 - Machine Learning](https://see.stanford.edu/Course/CS229), introduction to machine learning and statistical pattern recognition from Stanford.\n\n# Podcasts / Videos\n\n## Machine Learning\n\n- [Data Science at Home](https://datascienceathome.com) - A podcast about machine learning, artificial intelligence and algorithms.\n\n- [Yannic Kilcher](https://www.youtube.com/c/YannicKilcher/) - videos about machine learning research papers, programming, and issues of the AI community, and the broader impact of AI in society.\n\n- [AI Coffee Break with Letitia](https://www.youtube.com/c/AICoffeeBreak) - Lighthearted bite-sized Machine Learning videos for everyone\n\n## Statistics\n\n- [StatQuest](https://www.youtube.com/c/joshstarmer/) - breaks down the major methodologies into easy to understand pieces.\n\n## Mathematics\n\n- [3Blue1Brown](https://www.youtube.com/c/3blue1brown/) - some combination of math and entertainment. Difficult problems made simple with great animations.\n\n# Papers\n\n## Machine Learning\n\n- [arXiv.org \u003e cs \u003e cs.LG](https://arxiv.org/list/cs.LG/recent) - the latest scholarly articles in the field of machine learning (CS).\n\n- [arXiv.org \u003e stat \u003e stat.ML](https://arxiv.org/list/stat.ML/recent) - the latest scholarly articles in the field of machine learning (Stat).\n","funding_links":[],"categories":["Uncategorized"],"sub_categories":["Uncategorized"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frueedlinger%2Fml-resources","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frueedlinger%2Fml-resources","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frueedlinger%2Fml-resources/lists"}