{"id":13416219,"url":"https://github.com/instillai/machine-learning-course","last_synced_at":"2025-05-13T00:29:08.246Z","repository":{"id":37728673,"uuid":"170777132","full_name":"instillai/machine-learning-course","owner":"instillai","description":":speech_balloon: Machine Learning Course with Python: 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Machine Learning Course with Python\n###################################################\n\n.. image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat\n    :target: https://github.com/pyairesearch/machine-learning-for-everybody/pulls\n.. image:: https://badges.frapsoft.com/os/v2/open-source.png?v=103\n    :target: https://github.com/ellerbrock/open-source-badge/\n.. image:: https://img.shields.io/badge/Made%20with-Python-1f425f.svg\n      :target: https://www.python.org/\n.. image:: https://img.shields.io/github/contributors/machinelearningmindset/machine-learning-course.svg\n      :target: https://github.com/machinelearningmindset/machine-learning-course/graphs/contributors\n.. image:: https://img.shields.io/badge/book-pdf-blue.svg\n   :target: https://machinelearningmindset.com/wp-content/uploads/2019/06/machine-learning-course.pdf\n.. image:: https://img.shields.io/badge/official-documentation-green.svg\n   :target: https://machine-learning-course.readthedocs.io/en/latest/\n.. image:: https://img.shields.io/twitter/follow/machinemindset.svg?label=Follow\u0026style=social\n      :target: https://twitter.com/machinemindset\n\n\n\n\n\n\n##################\nTable of Contents\n##################\n.. contents::\n  :local:\n  :depth: 4\n\n\n================================================\nDownload Free Deep Learning Resource Guide\n================================================\n\n.. raw:: html\n\n   \u003cdiv align=\"center\"\u003e\n\n.. raw:: html\n\n  \u003ca href=\"https://www.machinelearningmindset.com/deep-learning-roadmap/\" target=\"_blank\"\u003e\n    \u003cimg width=\"723\" height=\"400\" align=\"center\" src=\"_img/deeplearningresource.png\"/\u003e\n  \u003c/a\u003e\n\n.. raw:: html\n\n   \u003c/div\u003e\n   \n\n================================================\nSlack Group\n================================================\n\n.. raw:: html\n\n   \u003cdiv align=\"center\"\u003e\n\n.. raw:: html\n\n \u003ca href=\"https://www.machinelearningmindset.com/slack-group/\" target=\"_blank\"\u003e\n  \u003cimg width=\"1033\" height=\"350\" align=\"center\" src=\"https://github.com/machinelearningmindset/TensorFlow-Course/blob/master/_img/0-welcome/joinslack.png\"/\u003e\n \u003c/a\u003e\n\n.. raw:: html\n\n   \u003c/div\u003e\n\n========================\nIntroduction\n========================\n\nThe purpose of this project is to provide a comprehensive and yet simple course in Machine Learning using Python.\n\n.. You can access to the full documentation with the following links: |Book| |Documentation|\n\n.. .. |Book| image:: https://img.shields.io/badge/book-pdf-blue.svg\n   :target: https://machinelearningmindset.com/wp-content/uploads/2019/06/machine-learning-course.pdf\n.. .. |Documentation| image:: https://img.shields.io/badge/official-documentation-green.svg\n   :target: https://machine-learning-course.readthedocs.io/en/latest/\n\n============\nMotivation\n============\n\n``Machine Learning``, as a tool for ``Artificial Intelligence``, is one of the most widely adopted\nscientific fields. A considerable amount of literature has been published on Machine Learning.\nThe purpose of this project is to provide the most important aspects of ``Machine Learning`` by presenting a\nseries of simple and yet comprehensive tutorials using ``Python``. In this project, we built our\ntutorials using many different well-known Machine Learning frameworks such as ``Scikit-learn``. In this project you will learn:\n\n* What is the definition of Machine Learning?\n* When it started and what is the trending evolution?\n* What are the Machine Learning categories and subcategories?\n* What are the mostly used Machine Learning algorithms and how to implement them?\n\n\n\n=====================\nMachine Learning\n=====================\n\n+--------------------------------------------------------------------+-------------------------------+\n| Title                                                              |    Document                   |\n+====================================================================+===============================+\n| An Introduction to Machine Learning                                |   `Overview \u003cIntro_\u003e`_        |\n+--------------------------------------------------------------------+-------------------------------+\n\n.. _Intro: docs/source/intro/intro.rst\n\n------------------------------------------------------------\nMachine Learning Basics\n------------------------------------------------------------\n\n.. figure:: _img/intro.png\n.. _lrtutorial: docs/source/content/overview/linear-regression.rst\n.. _lrcode: https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/linearRegressionOneVariable.ipynb\n\n.. _overtutorial: docs/source/content/overview/overfitting.rst\n.. _overcode: code/overview/overfitting\n\n.. _regtutorial: docs/source/content/overview/regularization.rst\n.. _regcode: code/overview/regularization\n\n.. _crosstutorial: docs/source/content/overview/crossvalidation.rst\n.. _crosscode: code/overview/cross-validation\n\n\n\n\n+--------------------------------------------------------------------+-------------------------------+--------------------------------+\n| Title                                                              |    Code                       |    Document                    |\n+====================================================================+===============================+================================+\n| Linear Regression                                                  | `Python \u003clrcode_\u003e`_           | `Tutorial \u003clrtutorial_\u003e`_      |\n+--------------------------------------------------------------------+-------------------------------+--------------------------------+\n| Overfitting / Underfitting                                         | `Python \u003covercode_\u003e`_         | `Tutorial \u003covertutorial_\u003e`_    |\n+--------------------------------------------------------------------+-------------------------------+--------------------------------+\n| Regularization                                                     | `Python \u003cregcode_\u003e`_          | `Tutorial \u003cregtutorial_\u003e`_     |\n+--------------------------------------------------------------------+-------------------------------+--------------------------------+\n| Cross-Validation                                                   | `Python \u003ccrosscode_\u003e`_        | `Tutorial \u003ccrosstutorial_\u003e`_   |\n+--------------------------------------------------------------------+-------------------------------+--------------------------------+\n\n\n------------------------------------------------------------\nSupervised learning\n------------------------------------------------------------\n\n.. figure:: _img/supervised.gif\n\n.. _dtdoc: docs/source/content/supervised/decisiontrees.rst\n.. _dtcode: code/supervised/DecisionTree/decisiontrees.py\n\n.. _knndoc: docs/source/content/supervised/knn.rst\n.. _knncode: code/supervised/KNN/knn.py\n\n.. _nbdoc: docs/source/content/supervised/bayes.rst\n.. _nbcode: code/supervised/Naive_Bayes\n\n.. _logisticrdoc: docs/source/content/supervised/logistic_regression.rst\n.. _logisticrcode: supervised/Logistic_Regression/logistic_ex1.py\n\n.. _linearsvmdoc: docs/source/content/supervised/linear_SVM.rst\n.. _linearsvmcode: code/supervised/Linear_SVM/linear_svm.py\n\n\n\n+--------------------------------------------------------------------+-------------------------------+------------------------------+\n| Title                                                              |    Code                       |    Document                  |\n+====================================================================+===============================+==============================+\n| Decision Trees                                                     | `Python \u003cdtcode_\u003e`_           | `Tutorial \u003cdtdoc_\u003e`_         |\n+--------------------------------------------------------------------+-------------------------------+------------------------------+\n| K-Nearest Neighbors                                                | `Python \u003cknncode_\u003e`_          | `Tutorial \u003cknndoc_\u003e`_        |\n+--------------------------------------------------------------------+-------------------------------+------------------------------+\n| Naive Bayes                                                        | `Python \u003cnbcode_\u003e`_           |  `Tutorial \u003cnbdoc_\u003e`_        |\n+--------------------------------------------------------------------+-------------------------------+------------------------------+\n| Logistic Regression                                                | `Python \u003clogisticrcode_\u003e`_    |  `Tutorial \u003clogisticrdoc_\u003e`_ |\n+--------------------------------------------------------------------+-------------------------------+------------------------------+\n| Support Vector Machines                                            | `Python \u003clinearsvmcode_\u003e`_    | `Tutorial \u003clinearsvmdoc_\u003e`_  |\n+--------------------------------------------------------------------+-------------------------------+------------------------------+\n\n\n\n\n------------------------------------------------------------\nUnsupervised learning\n------------------------------------------------------------\n\n.. figure:: _img/unsupervised.gif\n\n.. _clusteringdoc: docs/source/content/unsupervised/clustering.rst\n.. _clusteringcode: code/unsupervised/Clustering\n\n.. _pcadoc: docs/source/content/unsupervised/pca.rst\n.. _pcacode: code/unsupervised/PCA\n\n+--------------------------------------------------------------------+-------------------------------+--------------------------------+\n| Title                                                              |    Code                       |    Document                    |\n+====================================================================+===============================+================================+\n| Clustering                                                         | `Python \u003cclusteringcode_\u003e`_   | `Tutorial \u003cclusteringdoc_\u003e`_   |\n+--------------------------------------------------------------------+-------------------------------+--------------------------------+\n| Principal Components Analysis                                      | `Python \u003cpcacode_\u003e`_          | `Tutorial \u003cpcadoc_\u003e`_          |\n+--------------------------------------------------------------------+-------------------------------+--------------------------------+\n\n\n\n\n------------------------------------------------------------\nDeep Learning\n------------------------------------------------------------\n\n.. figure:: _img/deeplearning.png\n\n.. _mlpdoc: docs/source/content/deep_learning/mlp.rst\n.. _mlpcode: code/deep_learning/mlp\n\n\n.. _cnndoc: docs/source/content/deep_learning/cnn.rst\n.. _cnncode: code/deep_learning/cnn\n\n.. _aedoc: docs/source/content/deep_learning/autoencoder.rst\n.. _aecode: code/deep_learning/autoencoder\n\n.. _rnndoc: code/deep_learning/rnn/rnn.ipynb\n.. _rnncode: code/deep_learning/rnn/rnn.py\n\n\n+--------------------------------------------------------------------+-------------------------------+---------------------------+\n| Title                                                              |    Code                       |    Document               |\n+====================================================================+===============================+===========================+\n| Neural Networks Overview                                           |    `Python \u003cmlpcode_\u003e`_       |  `Tutorial \u003cmlpdoc_\u003e`_    |\n+--------------------------------------------------------------------+-------------------------------+---------------------------+\n| Convolutional Neural Networks                                      |    `Python \u003ccnncode_\u003e`_       | `Tutorial \u003ccnndoc_\u003e`_     |\n+--------------------------------------------------------------------+-------------------------------+---------------------------+\n| Autoencoders                                                       |    `Python \u003caecode_\u003e`_        | `Tutorial \u003caedoc_\u003e`_      |\n+--------------------------------------------------------------------+-------------------------------+---------------------------+\n| Recurrent Neural Networks                                          |    `Python \u003crnncode_\u003e`_       |  `IPython \u003crnndoc_\u003e`_     |\n+--------------------------------------------------------------------+-------------------------------+---------------------------+\n\n\n\n========================\nPull Request Process\n========================\n\nPlease consider the following criterions in order to help us in a better way:\n\n1. The pull request is mainly expected to be a link suggestion.\n2. Please make sure your suggested resources are not obsolete or broken.\n3. Ensure any install or build dependencies are removed before the end of the layer when doing a\n   build and creating a pull request.\n4. Add comments with details of changes to the interface, this includes new environment\n   variables, exposed ports, useful file locations and container parameters.\n5. You may merge the Pull Request in once you have the sign-off of at least one other developer, or if you\n   do not have permission to do that, you may request the owner to merge it for you if you believe all checks are passed.\n\n========================\nFinal Note\n========================\n\nWe are looking forward to your kind feedback. Please help us to improve this open source project and make our work better.\nFor contribution, please create a pull request and we will investigate it promptly. Once again, we appreciate\nyour kind feedback and support.\n\n\n========================\nDevelopers\n========================\n\n**Supervisor and creator of the project**: Amirsina Torfi [`GitHub\n\u003chttps://github.com/astorfi\u003e`_, `Personal Website\n\u003chttps://astorfi.github.io/\u003e`_, `Linkedin\n\u003chttps://www.linkedin.com/in/sinalk/\u003e`_ ]\n\n**Developers**: Amirsina Torfi, Brendan Sherman\\*, James E Hopkins\\* [`Linkedin \u003chttps://www.linkedin.com/in/jhopk\u003e`_], Zac Smith [`Linkedin \u003chttps://www.linkedin.com/in/zac-smith-a7bb60185/i\u003e`_]\n\n**NOTE**: This project has been developed as a capstone project offered by [`CS 4624 Multimedia/ Hypertext course at Virginia Tech \u003chttps://vtechworks.lib.vt.edu/handle/10919/90655\u003e`_] and\nSupervised and supported by [`Machine Learning Mindset \u003chttps://machinelearningmindset.com/\u003e`_].\n\n\\*: equally contributed\n\n======================\nCitation\n======================\n\nIf you found this course useful, please kindly consider citing it as below:\n\n.. code:: shell\n\n    @software{amirsina_torfi_2019_3585763,\n      author       = {Amirsina Torfi and\n                      Brendan Sherman and\n                      Jay Hopkins and\n                      Eric Wynn and\n                      hokie45 and\n                      Frederik De Bleser and\n                      李明岳 and\n                      Samuel Husso and\n                      Alain},\n      title        = {{machinelearningmindset/machine-learning-course: \n                       Machine Learning with Python}},\n      month        = dec,\n      year         = 2019,\n      publisher    = {Zenodo},\n      version      = {1.0},\n      doi          = {10.5281/zenodo.3585763},\n      url          = {https://doi.org/10.5281/zenodo.3585763}\n    }\n","funding_links":[],"categories":["Python","A01_机器学习教程","📚 Project Purpose"],"sub_categories":["Machine Learning (Entry-Level)"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finstillai%2Fmachine-learning-course","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Finstillai%2Fmachine-learning-course","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finstillai%2Fmachine-learning-course/lists"}