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machinelearningmindset /machine-learning-course

:speech_balloon: Machine Learning Course with Python:

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A Machine Learning Course with Python

.. image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat :target: https://github.com/pyairesearch/machine-learning-for-everybody/pulls .. image:: https://badges.frapsoft.com/os/v2/open-source.png?v=103 :target: https://github.com/ellerbrock/open-source-badge/ .. image:: https://img.shields.io/badge/Made%20with-Python-1f425f.svg :target: https://www.python.org/ .. image:: https://img.shields.io/github/contributors/machinelearningmindset/machine-learning-course.svg :target: https://github.com/machinelearningmindset/machine-learning-course/graphs/contributors .. image:: https://img.shields.io/badge/book-pdf-blue.svg :target: https://machinelearningmindset.com/wp-content/uploads/2019/06/machine-learning-course.pdf .. image:: https://img.shields.io/badge/official-documentation-green.svg :target: https://machine-learning-course.readthedocs.io/en/latest/ .. image:: https://img.shields.io/twitter/follow/machinemindset.svg?label=Follow&style=social :target: https://twitter.com/machinemindset

Table of Contents

.. contents:: :local: :depth: 4

================================================

Download Free Deep Learning Resource Guide

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Slack Group

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========================

Introduction

The purpose of this project is to provide a comprehensive and yet simple course in Machine Learning using Python.

.. You can access to the full documentation with the following links: |Book| |Documentation|

.. .. |Book| image:: https://img.shields.io/badge/book-pdf-blue.svg :target: https://machinelearningmindset.com/wp-content/uploads/2019/06/machine-learning-course.pdf .. .. |Documentation| image:: https://img.shields.io/badge/official-documentation-green.svg :target: https://machine-learning-course.readthedocs.io/en/latest/

============

Motivation

Machine Learning

, as a tool for

Artificial Intelligence

, is one of the most widely adopted scientific fields. A considerable amount of literature has been published on Machine Learning. The purpose of this project is to provide the most important aspects of

Machine Learning

by presenting a series of simple and yet comprehensive tutorials using

Python

. In this project, we built our tutorials using many different well-known Machine Learning frameworks such as

Scikit-learn

. In this project you will learn:

  • What is the definition of Machine Learning?
  • When it started and what is the trending evolution?
  • What are the Machine Learning categories and subcategories?
  • What are the mostly used Machine Learning algorithms and how to implement them?

=====================

Machine Learning

+--------------------------------------------------------------------+-------------------------------+ | Title | Document | +====================================================================+===============================+ | An Introduction to Machine Learning |

Overview <intro_></intro_>

_ | +--------------------------------------------------------------------+-------------------------------+

.. _Intro: docs/source/intro/intro.rst


Machine Learning Basics

.. figure:: _img/intro.png .. _lrtutorial: docs/source/content/overview/linear-regression.rst .. _lrcode: https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/linearRegressionOneVariable.ipynb

.. _overtutorial: docs/source/content/overview/overfitting.rst .. _overcode: code/overview/overfitting

.. _regtutorial: docs/source/content/overview/regularization.rst .. _regcode: code/overview/regularization

.. _crosstutorial: docs/source/content/overview/crossvalidation.rst .. _crosscode: code/overview/cross-validation

+--------------------------------------------------------------------+-------------------------------+--------------------------------+ | Title | Code | Document | +====================================================================+===============================+================================+ | Linear Regression |

Python <lrcode_></lrcode_>

_ |

Tutorial <lrtutorial_></lrtutorial_>

_ | +--------------------------------------------------------------------+-------------------------------+--------------------------------+ | Overfitting / Underfitting |

Python <overcode_></overcode_>

_ |

Tutorial <overtutorial_></overtutorial_>

_ | +--------------------------------------------------------------------+-------------------------------+--------------------------------+ | Regularization |

Python <regcode_></regcode_>

_ |

Tutorial <regtutorial_></regtutorial_>

_ | +--------------------------------------------------------------------+-------------------------------+--------------------------------+ | Cross-Validation |

Python <crosscode_></crosscode_>

_ |

Tutorial <crosstutorial_></crosstutorial_>

_ | +--------------------------------------------------------------------+-------------------------------+--------------------------------+


Supervised learning

.. figure:: _img/supervised.gif

.. _dtdoc: docs/source/content/supervised/decisiontrees.rst .. _dtcode: code/supervised/DecisionTree/decisiontrees.py

.. _knndoc: docs/source/content/supervised/knn.rst .. _knncode: code/supervised/KNN/knn.py

.. _nbdoc: docs/source/content/supervised/bayes.rst .. _nbcode: code/supervised/Naive_Bayes

.. _logisticrdoc: docs/source/content/supervised/logistic_regression.rst .. _logisticrcode: supervised/Logistic_Regression/logistic_ex1.py

.. _linearsvmdoc: docs/source/content/supervised/linear_SVM.rst .. _linearsvmcode: code/supervised/Linear_SVM/linear_svm.py

+--------------------------------------------------------------------+-------------------------------+------------------------------+ | Title | Code | Document | +====================================================================+===============================+==============================+ | Decision Trees |

Python <dtcode_></dtcode_>

_ |

Tutorial <dtdoc_></dtdoc_>

_ | +--------------------------------------------------------------------+-------------------------------+------------------------------+ | K-Nearest Neighbors |

Python <knncode_></knncode_>

_ |

Tutorial <knndoc_></knndoc_>

_ | +--------------------------------------------------------------------+-------------------------------+------------------------------+ | Naive Bayes |

Python <nbcode_></nbcode_>

_ |

Tutorial <nbdoc_></nbdoc_>

_ | +--------------------------------------------------------------------+-------------------------------+------------------------------+ | Logistic Regression |

Python <logisticrcode_></logisticrcode_>

_ |

Tutorial <logisticrdoc_></logisticrdoc_>

_ | +--------------------------------------------------------------------+-------------------------------+------------------------------+ | Support Vector Machines |

Python <linearsvmcode_></linearsvmcode_>

_ |

Tutorial <linearsvmdoc_></linearsvmdoc_>

_ | +--------------------------------------------------------------------+-------------------------------+------------------------------+


Unsupervised learning

.. figure:: _img/unsupervised.gif

.. _clusteringdoc: docs/source/content/unsupervised/clustering.rst .. _clusteringcode: code/unsupervised/Clustering

.. _pcadoc: docs/source/content/unsupervised/pca.rst .. _pcacode: code/unsupervised/PCA

+--------------------------------------------------------------------+-------------------------------+--------------------------------+ | Title | Code | Document | +====================================================================+===============================+================================+ | Clustering |

Python <clusteringcode_></clusteringcode_>

_ |

Tutorial <clusteringdoc_></clusteringdoc_>

_ | +--------------------------------------------------------------------+-------------------------------+--------------------------------+ | Principal Components Analysis |

Python <pcacode_></pcacode_>

_ |

Tutorial <pcadoc_></pcadoc_>

_ | +--------------------------------------------------------------------+-------------------------------+--------------------------------+


Deep Learning

.. figure:: _img/deeplearning.png

.. _mlpdoc: docs/source/content/deep_learning/mlp.rst .. _mlpcode: code/deep_learning/mlp

.. _cnndoc: docs/source/content/deep_learning/cnn.rst .. _cnncode: code/deep_learning/cnn

.. _aedoc: docs/source/content/deep_learning/autoencoder.rst .. _aecode: code/deep_learning/autoencoder

.. _rnndoc: code/deep_learning/rnn/rnn.ipynb .. _rnncode: code/deep_learning/rnn/rnn.py

+--------------------------------------------------------------------+-------------------------------+---------------------------+ | Title | Code | Document | +====================================================================+===============================+===========================+ | Neural Networks Overview |

Python <mlpcode_></mlpcode_>

_ |

Tutorial <mlpdoc_></mlpdoc_>

_ | +--------------------------------------------------------------------+-------------------------------+---------------------------+ | Convolutional Neural Networks |

Python <cnncode_></cnncode_>

_ |

Tutorial <cnndoc_></cnndoc_>

_ | +--------------------------------------------------------------------+-------------------------------+---------------------------+ | Autoencoders |

Python <aecode_></aecode_>

_ |

Tutorial <aedoc_></aedoc_>

_ | +--------------------------------------------------------------------+-------------------------------+---------------------------+ | Recurrent Neural Networks |

Python <rnncode_></rnncode_>

_ |

IPython <rnndoc_></rnndoc_>

_ | +--------------------------------------------------------------------+-------------------------------+---------------------------+

========================

Pull Request Process

Please consider the following criterions in order to help us in a better way:

  1. The pull request is mainly expected to be a link suggestion.
  2. Please make sure your suggested resources are not obsolete or broken.
  3. Ensure any install or build dependencies are removed before the end of the layer when doing a build and creating a pull request.
  4. Add comments with details of changes to the interface, this includes new environment variables, exposed ports, useful file locations and container parameters.
  5. You may merge the Pull Request in once you have the sign-off of at least one other developer, or if you do not have permission to do that, you may request the owner to merge it for you if you believe all checks are passed.

========================

Final Note

We are looking forward to your kind feedback. Please help us to improve this open source project and make our work better. For contribution, please create a pull request and we will investigate it promptly. Once again, we appreciate your kind feedback and support.

========================

Developers

Creator: Machine Learning Mindset [

Blog<https:></https:>

_,

GitHub<https:></https:>
```_ , 

Twitterhttps:

\_]

**Supervisor**: Amirsina Torfi [

GitHubhttps:

_, 

Personal Websitehttps:

Linkedinhttps:

\_ ]

**Developers**: Brendan Sherman\*, James E Hopkins\* [

Linkedin https:

_], Zac Smith [

Linkedin https:


**NOTE**: This project has been developed as a capstone project offered by [

CS 4624 Multimedia/ Hypertext course at Virginia Tech https:

_] and Supervised and supported by [

Machine Learning Mindset https:


\*: equally contributed

======================

# Citation

If you found this course useful, please kindly consider citing it as below:

.. code:: shell

@software{amirsina_torfi_2019_3585763, author = {Amirsina Torfi and Brendan Sherman and Jay Hopkins and Eric Wynn and hokie45 and Frederik De Bleser and 李明岳 and Samuel Husso and Alain}, title = {{machinelearningmindset/machine-learning-course: Machine Learning with Python}}, month = dec, year = 2019, publisher = {Zenodo}, version = {1.0}, doi = {10.5281/zenodo.3585763}, url = {https://doi.org/10.5281/zenodo.3585763} } ```

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