{"id":13399723,"url":"https://github.com/amueller/introduction_to_ml_with_python","last_synced_at":"2025-05-12T13:23:48.974Z","repository":{"id":37269961,"uuid":"59957955","full_name":"amueller/introduction_to_ml_with_python","owner":"amueller","description":"Notebooks and code for the book \"Introduction to Machine Learning with Python\"","archived":false,"fork":false,"pushed_at":"2024-03-14T02:46:03.000Z","size":186383,"stargazers_count":7678,"open_issues_count":25,"forks_count":4624,"subscribers_count":370,"default_branch":"main","last_synced_at":"2025-04-23T16:07:52.449Z","etag":null,"topics":[],"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/amueller.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-05-29T18:29:47.000Z","updated_at":"2025-04-22T16:16:44.000Z","dependencies_parsed_at":"2024-05-15T23:03:31.346Z","dependency_job_id":"24988983-50ef-47ea-bd20-e84f7beace08","html_url":"https://github.com/amueller/introduction_to_ml_with_python","commit_stats":{"total_commits":88,"total_committers":19,"mean_commits":4.631578947368421,"dds":0.6931818181818181,"last_synced_commit":"842e623dc16d194cb23c19882fc013e6b425d443"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amueller%2Fintroduction_to_ml_with_python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amueller%2Fintroduction_to_ml_with_python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amueller%2Fintroduction_to_ml_with_python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amueller%2Fintroduction_to_ml_with_python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/amueller","download_url":"https://codeload.github.com/amueller/introduction_to_ml_with_python/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253745553,"owners_count":21957398,"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":[],"created_at":"2024-07-30T19:00:41.894Z","updated_at":"2025-05-12T13:23:48.951Z","avatar_url":"https://github.com/amueller.png","language":"Jupyter Notebook","funding_links":[],"categories":["Jupyter Notebook","Python","Interesting Non EO parts Python","Python, IPython, Scikit-learn etc.","📚 Project Purpose"],"sub_categories":["General-Purpose Machine Learning","GEDI","Ukraine","Machine Learning (Entry-Level)"],"readme":"[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/amueller/introduction_to_ml_with_python/master)\n\n# Introduction to Machine Learning with Python\n\nThis repository holds the code for the forthcoming book \"Introduction to Machine\nLearning with Python\" by [Andreas Mueller](http://amueller.io) and [Sarah Guido](https://twitter.com/sarah_guido).\nYou can find details about the book on the [O'Reilly website](http://shop.oreilly.com/product/0636920030515.do).\n\nThe book requires the current stable version of scikit-learn, that is\n0.20.0.  Most of the book can also be used with previous versions of\nscikit-learn, though you need to adjust the import for everything from the\n``model_selection`` module, mostly ``cross_val_score``, ``train_test_split``\nand ``GridSearchCV``.\n\n\nThis repository provides the notebooks from which the book is created, together\nwith the ``mglearn`` library of helper functions to create figures and\ndatasets.\n\nFor the curious ones, the cover depicts a [hellbender](https://en.wikipedia.org/wiki/Hellbender).\n\nAll datasets are included in the repository, with the exception of the aclImdb dataset, which you can download from\nthe page of [Andrew Maas](http://ai.stanford.edu/~amaas/data/sentiment/). See the book for details.\n\nIf you get ``ImportError: No module named mglearn`` you can try to install mglearn into your python environment using\nthe command ``pip install mglearn`` in your terminal or ``!pip install mglearn`` in Jupyter Notebook.\n\n\n## Errata\nPlease note that the first print of the book is missing the following line when listing the assumed imports:\n\n```python\nfrom IPython.display import display\n```\nPlease add this line if you see an error involving ``display``.\n\n\nThe first print of the book used a function called ``plot_group_kfold``.\nThis has been renamed to ``plot_label_kfold`` because of a rename in\nscikit-learn.\n\n## Setup\n\nTo run the code, you need the packages ``numpy``, ``scipy``, ``scikit-learn``, ``matplotlib``, ``pandas`` and ``pillow``.\nSome of the visualizations of decision trees and neural networks structures also require ``graphviz``. The chapter\non text processing also requires ``nltk`` and ``spacy``.\n\nThe easiest way to set up an environment is by installing [Anaconda](https://www.continuum.io/downloads).\n\n### Installing packages with conda:\nIf you already have a Python environment set up, and you are using the ``conda`` package manager, you can get all packages by running\n\n    conda install numpy scipy scikit-learn matplotlib pandas pillow graphviz python-graphviz\n\nFor the chapter on text processing you also need to install ``nltk`` and ``spacy``:\n\n    conda install nltk spacy\n\n\n### Installing packages with pip\nIf you already have a Python environment and are using pip to install packages, you need to run\n\n    pip install numpy scipy scikit-learn matplotlib pandas pillow graphviz\n\nYou also need to install the graphiz C-library, which is easiest using a package manager.\nIf you are using OS X and homebrew, you can ``brew install graphviz``. If you are on Ubuntu or debian, you can ``apt-get install graphviz``.\nInstalling graphviz on Windows can be tricky and using conda / anaconda is recommended.\nFor the chapter on text processing you also need to install ``nltk`` and ``spacy``:\n\n    pip install nltk spacy\n\n### Downloading English language model\nFor the text processing chapter, you need to download the English language model for spacy using\n\n    python -m spacy download en\n\n## Submitting Errata\n\nIf you have errata for the (e-)book, please submit them via the [O'Reilly Website](http://www.oreilly.com/catalog/errata.csp?isbn=0636920030515).\nYou can submit fixes to the code as pull-requests here, but I'd appreciate it if you would also submit them there, as this repository doesn't hold the\n\"master notebooks\".\n\n![cover](cover.jpg)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famueller%2Fintroduction_to_ml_with_python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famueller%2Fintroduction_to_ml_with_python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famueller%2Fintroduction_to_ml_with_python/lists"}