{"id":13934924,"url":"https://github.com/krasserm/machine-learning-notebooks","last_synced_at":"2025-04-06T18:17:47.105Z","repository":{"id":53894728,"uuid":"98178273","full_name":"krasserm/machine-learning-notebooks","owner":"krasserm","description":"Stanford Machine Learning course exercises implemented with scikit-learn","archived":false,"fork":false,"pushed_at":"2020-11-18T21:19:49.000Z","size":24257,"stargazers_count":346,"open_issues_count":2,"forks_count":146,"subscribers_count":21,"default_branch":"master","last_synced_at":"2025-03-30T17:11:15.280Z","etag":null,"topics":["machine-learning","scikit-learn"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","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/krasserm.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":"2017-07-24T10:21:05.000Z","updated_at":"2025-03-19T21:25:04.000Z","dependencies_parsed_at":"2022-08-13T03:31:03.768Z","dependency_job_id":null,"html_url":"https://github.com/krasserm/machine-learning-notebooks","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/krasserm%2Fmachine-learning-notebooks","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/krasserm%2Fmachine-learning-notebooks/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/krasserm%2Fmachine-learning-notebooks/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/krasserm%2Fmachine-learning-notebooks/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/krasserm","download_url":"https://codeload.github.com/krasserm/machine-learning-notebooks/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247526770,"owners_count":20953143,"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":["machine-learning","scikit-learn"],"created_at":"2024-08-07T23:01:18.869Z","updated_at":"2025-04-06T18:17:47.084Z","avatar_url":"https://github.com/krasserm.png","language":"Jupyter Notebook","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"readme":"## Machine learning notebooks\n\nThis project contains solutions to the [Stanford Machine Learning](https://www.coursera.org/learn/machine-learning) \ncourse exercises implemented with [Python](https://www.python.org/) and [scikit-learn](http://scikit-learn.org/). The scikit-learn \nmachine learning library provides optimized implementations for all algorithms presented in the course and needed in \nthe course exercises. Instead of writing low-level [Octave](https://www.gnu.org/software/octave/) code, as required by \nthe course, the solutions presented here demonstrate how to use scikit-learn to solve these exercises on a much higher \nlevel. It is a level that is closer to that of real-world machine learning projects. This project respects the \n[Coursera Honor Code](https://learner.coursera.help/hc/en-us/articles/209818863-Coursera-Honor-Code) as the presented \nsolutions can't be used to derive the lower-level Octave code that must be written to complete the assignments. \n\nI developed these solutions while learning Python and its \n[scientific programming libraries](https://www.scipy.org/) such as [NumPy](http://www.numpy.org/), \n[SciPy](https://scipy.org/scipylib/index.html), [pandas](http://pandas.pydata.org/) and \n[matplotlib](http://matplotlib.org/) in a machine learning context. The solutions are provided as \n[Jupyter](http://jupyter.org/) notebooks. Developers new to scikit-learn hopefully find them useful to see how \nthe machine learning topics covered in the course relate to the \n[scikit-learn API](http://scikit-learn.org/stable/modules/classes.html). In their current state, the notebooks neither \nexplain machine learning basics nor introduce the used libraries. For learning machine learning basics I highly \nrecommend attending the course lectures. For an introduction to the used libraries the following tutorials are a good \nstarting point: \n\n- [Python tutorial](https://docs.python.org/3/tutorial/)\n- [NumPy tutorial](https://numpy.org/doc/stable/user/quickstart.html)\n- [SciPy tutorial](https://docs.scipy.org/doc/scipy/reference/tutorial/index.html)\n- [Pandas tutorial](http://pandas.pydata.org/pandas-docs/stable/10min.html)\n- [Pyplot tutorial](http://matplotlib.org/users/pyplot_tutorial.html)\n- [Scikit-learn tutorials](http://scikit-learn.org/stable/tutorial/index.html)\n\n### Course exercises\n\n- [Exercise 1 notebook](ml-ex1.ipynb): Linear regression ([ex1.pdf](data/ml-ex1/ex1.pdf))\n- [Exercise 2 notebook](ml-ex2.ipynb): Logistic regression ([ex2.pdf](data/ml-ex2/ex2.pdf))\n- [Exercise 3 notebook](ml-ex3.ipynb): Multi-class classification and neural networks ([ex3.pdf](data/ml-ex3/ex3.pdf))\n- [Exercise 4 notebook](ml-ex4.ipynb): Neural networks learning ([ex4.pdf](data/ml-ex4/ex4.pdf))\n- [Exercise 5 notebook](ml-ex5.ipynb): Regularized linear regression and bias vs. variance ([ex5.pdf](data/ml-ex5/ex5.pdf))\n- [Exercise 6 notebook](ml-ex6.ipynb): Support vector machines ([ex6.pdf](data/ml-ex6/ex6.pdf))\n- [Exercise 7 notebook](ml-ex7.ipynb): K-means clustering and principal component analysis ([ex7.pdf](data/ml-ex7/ex7.pdf))\n- [Exercise 8 notebook](ml-ex8.ipynb): Anomaly detection and recommender systems ([ex8.pdf](data/ml-ex8/ex8.pdf))\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkrasserm%2Fmachine-learning-notebooks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkrasserm%2Fmachine-learning-notebooks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkrasserm%2Fmachine-learning-notebooks/lists"}