{"id":14379,"url":"https://github.com/ugwaj/awesome-machine-learning-python","name":"awesome-machine-learning-python","description":"Machine and Deep Learning in Python","projects_count":53,"last_synced_at":"2026-06-14T02:00:35.014Z","repository":{"id":85097447,"uuid":"39134242","full_name":"ugwaj/awesome-machine-learning-python","owner":"ugwaj","description":"Machine and Deep Learning in Python","archived":false,"fork":false,"pushed_at":"2015-07-20T07:42:46.000Z","size":190,"stargazers_count":6,"open_issues_count":0,"forks_count":88,"subscribers_count":2,"default_branch":"master","last_synced_at":"2026-05-28T11:03:13.044Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":false,"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/ugwaj.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2015-07-15T11:56:10.000Z","updated_at":"2020-05-17T13:39:35.000Z","dependencies_parsed_at":"2023-03-02T06:45:36.211Z","dependency_job_id":null,"html_url":"https://github.com/ugwaj/awesome-machine-learning-python","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ugwaj/awesome-machine-learning-python","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ugwaj%2Fawesome-machine-learning-python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ugwaj%2Fawesome-machine-learning-python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ugwaj%2Fawesome-machine-learning-python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ugwaj%2Fawesome-machine-learning-python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ugwaj","download_url":"https://codeload.github.com/ugwaj/awesome-machine-learning-python/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ugwaj%2Fawesome-machine-learning-python/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34306772,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-14T02:00:07.365Z","response_time":62,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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"}},"created_at":"2024-01-12T20:23:55.263Z","updated_at":"2026-06-14T02:00:35.015Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Alghoritms","Text Analysis","Learning Machine Learning","Business","Bullying","Gaming","Recommendations","Visualization","Video Streaming","Time","Audio","Articles","Lambda","Short Articles","Image recognition","Python \u0026 Machine Learning","Videos","Deep Learning Frameworks","Tools"],"sub_categories":["Fraud Detection","Material Databases","Use Case Examples","Pyplot","Courses","Chat","Sport","Image Recognition","Random","Generic","Law","Libraries"],"readme":"# Machine and Deep Learning with Python\n\n## Business\n\n* [Estimating a Real Business Cycle DSGE Model by Maximum Likelihood in Python](http://nbviewer.ipython.org/gist/ChadFulton/fbce8efd41fcf271b316)\n\n## Bullying\n\n* [Understanding and fighting bullying with machine learning](http://research.cs.wisc.edu/bullying)\n\n## Gaming\n\n* [Artificial intelligence learns Mario level in just 34 attempts](http://www.engadget.com/2015/06/17/super-mario-world-self-learning-ai)\n\n## Recommendations \n\n* [Collaborative filtering recommendation engine implementation in python](http://dataaspirant.com/2015/05/25/collaborative-filtering-recommendation-engine-implementation-in-python)\n* [NLP in python -- predicting HN upvotes from headlines](http://blog.dataquest.io/blog/predicting-upvotes)\n\n## Text Analysis\n\n* [Adam Palay - \"Words, words, words\": Reading Shakespeare with Python - PyCon 2015](https://www.youtube.com/watch?v=EoWG0lavg9U)\n* [High-quality XML versions of the complete works of Shakespeare](https://github.com/severdia/PlayShakespeare.com-XML)\n* [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)\n* [Document Clustering with Python](http://nbviewer.ipython.org/github/brandomr/document_cluster/blob/master/cluster_analysis_web.ipynb)\n\n## Money \n\n* [Predicting Heavy and Extreme Losses in Real-Time for Portfolio Holders] (http://www.quantatrisk.com/2015/06/14/predicting-heavy-extreme-losses-portfolio-1)\n\n## Visualization\n\n### Pyplot\n\n* [Pyplot tutorial](http://matplotlib.org/users/pyplot_tutorial.html)\n* [Plotly for IPython Notebooks](https://dato.com/learn/gallery/notebooks/food_retrieval-public.html)\n\n\n## Deep Learning Frameworks\n\n* [NErvana's pythON based Deep Learning Framework](https://github.com/NervanaSystems/neon)\n\n## Video Streaming \n\n### Use Case Examples\n\n* [Target acquired: Finding targets in drone and quadcopter video streams using Python and OpenCV](http://www.pyimagesearch.com/2015/05/04/target-acquired-finding-targets-in-drone-and-quadcopter-video-streams-using-python-and-opencv)\n* [Visualization of taxi trip end points](https://www.kaggle.com/hochthom/pkdd-15-predict-taxi-service-trajectory-i/visualization-of-taxi-trip-end-points)\n* [Basic motion detection and tracking with Python and OpenCV](http://www.pyimagesearch.com/2015/05/25/basic-motion-detection-and-tracking-with-python-and-opencv)\n* [Home surveillance and motion detection with the Raspberry Pi, Python, OpenCV, and Dropbox](http://www.pyimagesearch.com/2015/06/01/home-surveillance-and-motion-detection-with-the-raspberry-pi-python-and-opencv)\n\n## Time\n\n* [Making Space Time Predictions using Python and Spark](https://www.youtube.com/watch?v=0YTIOn7_h_k)\n\n## Audio\n\n* [Classifying and Visualizing Musical Pitch with K-means Clustering](http://www.galvanize.com/blog/2015/05/28/classifying-and-visualizing-musical-pitch-with-k-means-clustering)\n\n## Learning Machine Learning\n\n* [Supervised learning superstitions cheat sheet](http://ryancompton.net/assets/ml_cheat_sheet/supervised_learning.html)\n* [Introduction to Deep Learning with Python](https://www.youtube.com/watch?v=S75EdAcXHKk)\n* [How to implement a neural network](http://peterroelants.github.io/posts/neural_network_implementation_part01)\n* [How to build and run your first deep learning network]\n(https://beta.oreilly.com/learning/how-to-build-and-run-your-first-deep-learning-network)\n* [Neural Nets for Newbies by Melanie Warrick](https://www.youtube.com/watch?v=Cu6A96TUy_o)\n* [Data Science 101: Interactive Analysis with Jupyter, Pandas and Treasure Data](http://blog.treasuredata.com/blog/2015/06/23/data-science-101-interactive-analysis-with-jupyter-pandas-and-treasure-data)\n* [Deep Learning Tutorial](http://videolectures.net/kdd2014_salakhutdinov_deep_learning)\n\n### Material Databases\n\n* [Materials for Learning Machine Learning](http://www.jacksimpson.co/2015/06/07/materials-for-learning-machine-learning)\n* [On Deep Learning\nA Tweeted Bibliography](https://medium.com/deep-learning-101/on-deep-learning-a-tweeted-bibliography-68ab095376e7)\n* [Continually updated Data Science Python Notebooks](https://github.com/donnemartin/data-science-ipython-notebooks)\n* [http://people.duke.edu/~ccc14/sta-663/index.html](http://people.duke.edu/~ccc14/sta-663/index.html) \n* [Stanford Reports for 2015](http://cs224d.stanford.edu/reports.html)\n* [Data Science Specialization](http://datasciencespecialization.github.io)\n* [Unsupervised Feature Learning and Deep Learning](http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=ufldl)\n* [Awesome Deep Vistion](https://github.com/szwed/awesome-deep-vision)\n\n#### Cheatsheets\n\n* [8 Best Machine Learning Cheat Sheets](http://designimag.com/best-machine-learning-cheat-sheets)\n\n### Courses\n\n* [Deep Learning Lecture - University of Oxford](http://www.computervisiontalks.com/tag/deep-learning-course/)\n\n# Theory\n\n## Articles\n\n* [Machine Learning and Law - Harry Surden](http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2417415)\n* [eBrevia Applies Machine Learning To Contract Review](http://www.forbes.com/sites/benkepes/2015/02/20/ebrevia-applies-machine-learning-to-contract-review/)\n* [Introduction to Neural Machine Translation with GPUs](http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus)\n\n## Alghoritms\n\n### Fraud Detection\n\n* [Detecting Fraudulent Personalities in Networks of Online Auctioneers](http://www.cs.cmu.edu/~dchau/papers/auction_fraud_pkdd06.pdf)\n\n### Chat\n\n* [A Neural Conversational Model](http://arxiv.org/pdf/1506.05869v1.pdf)\n\n### Sport\n\n* [Prediction and Quantification of Individual Athletic Performance](http://arxiv.org/pdf/1505.01147v2.pdf)\n\n### Image Recognition\n\n* [Generative Image Modeling Using Spatial LSTMs](http://arxiv.org/pdf/1506.03478v1.pdf)\n* [Suddenly, a leopard print sofa appears](http://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.html)\n### Random\n\n* [On the accuracy of self-normalized log-linear models](http://arxiv.org/pdf/1506.04147v1.pdf)\n* [Bayesian Dark Knowledge](http://arxiv.org/pdf/1506.04416v1.pdf)\n\n# Amazon AWS\n\n## Lambda\n\n* [The future is now, and it's using AWS Lambda](http://lg.io/2015/05/16/the-future-is-now-and-its-using-aws-lambda.html)\n\n# Propaganda\n\n## Short Articles\n\n* [Machines that think for themselves](http://www.work.caltech.edu/paper/sciam2012.pdf)\n* [How Artificial Intelligence Will Make Technology Disappear](https://medium.com/using-artificial-intelligence-to-make-technology/how-artificial-intelligence-will-make-technology-disappear-503cd88e1e6a)\n* [Deep Learning Machine Beats Humans in IQ Test](http://www.technologyreview.com/view/538431/deep-learning-machine-beats-humans-in-iq-test/)\n\n## Image recognition\n\n* [What’s in This Picture? AI Becomes as Smart as a Toddler](http://www.bloomberg.com/news/articles/2015-05-22/what-s-in-this-picture-ai-becomes-as-smart-as-a-toddler)\n* [Bringing Deep Learning to the Grocery Store](https://dato.com/learn/gallery/notebooks/food_retrieval-public.html)\n* [PyImageSearch and Computer Vision] (http://www.talkpythontome.com/episodes/show/11/pyimagesearch-and-computer-vision)\n\n## Python \u0026 Machine Learning\n\n* [Python-Powered Machine Learning in the Cloud](http://www.pyvideo.org/video/3556/python-powered-machine-learning-in-the-cloud)\n\n## Videos\n\n### Generic\n\n* [Humans Need Not Apply](https://www.youtube.com/watch?t=490\u0026v=7Pq-S557XQU)\n\n### Law\n\n* [Professor Harry Surden Discusses Machine Learning within Law](https://www.youtube.com/watch?v=sOLXOsiX0Qk)\n\n### Social Risks\n\n* [Robot Economics] (https://www.youtube.com/watch?v=QGxH35SKInM)\n\n## Tools \n\n### Libraries\n\n* [A library to build and test machine learning features] (https://pypi.python.org/pypi/featureforge/0.1.6)\n* [deepy: Highly extensible deep learning framework based on Theano](https://github.com/uaca/deepy)\n\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/ugwaj%2Fawesome-machine-learning-python/projects"}