:speech_balloon: Machine Learning Course with Python:
################################################### A Machine Learning Course with Python ###################################################
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################## Table of Contents ################## .. contents:: :local: :depth: 4
.. raw:: html
.. raw:: html
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/
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:
+--------------------------------------------------------------------+-------------------------------+
| Title | Document |
+====================================================================+===============================+
| An Introduction to Machine Learning | Overview <Intro_>
_ |
+--------------------------------------------------------------------+-------------------------------+
.. _Intro: docs/source/intro/intro.rst
.. 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_>
_ | Tutorial <lrtutorial_>
_ |
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
| Overfitting / Underfitting | Python <overcode_>
_ | Tutorial <overtutorial_>
_ |
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
| Regularization | Python <regcode_>
_ | Tutorial <regtutorial_>
_ |
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
| Cross-Validation | Python <crosscode_>
_ | Tutorial <crosstutorial_>
_ |
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
.. 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_>
_ | Tutorial <dtdoc_>
_ |
+--------------------------------------------------------------------+-------------------------------+------------------------------+
| K-Nearest Neighbors | Python <knncode_>
_ | Tutorial <knndoc_>
_ |
+--------------------------------------------------------------------+-------------------------------+------------------------------+
| Naive Bayes | Python <nbcode_>
_ | Tutorial <nbdoc_>
_ |
+--------------------------------------------------------------------+-------------------------------+------------------------------+
| Logistic Regression | Python <logisticrcode_>
_ | Tutorial <logisticrdoc_>
_ |
+--------------------------------------------------------------------+-------------------------------+------------------------------+
| Support Vector Machines | Python <linearsvmcode_>
_ | Tutorial <linearsvmdoc_>
_ |
+--------------------------------------------------------------------+-------------------------------+------------------------------+
.. 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_>
_ | Tutorial <clusteringdoc_>
_ |
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
| Principal Components Analysis | Python <pcacode_>
_ | Tutorial <pcadoc_>
_ |
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
.. 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_>
_ | Tutorial <mlpdoc_>
_ |
+--------------------------------------------------------------------+-------------------------------+---------------------------+
| Convolutional Neural Networks | Python <cnncode_>
_ | Tutorial <cnndoc_>
_ |
+--------------------------------------------------------------------+-------------------------------+---------------------------+
| Autoencoders | Python <aecode_>
_ | Tutorial <aedoc_>
_ |
+--------------------------------------------------------------------+-------------------------------+---------------------------+
| Recurrent Neural Networks | Python <rnncode_>
_ | IPython <rnndoc_>
_ |
+--------------------------------------------------------------------+-------------------------------+---------------------------+
Please consider the following criterions in order to help us in a better way:
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.
Creator: Machine Learning Mindset [Blog <https://machinelearningmindset.com/blog/>
, GitHub <https://github.com/machinelearningmindset>
, Twitter <https://twitter.com/machinemindset>
_]
Supervisor: Amirsina Torfi [GitHub <https://github.com/astorfi>
, Personal Website <https://astorfi.github.io/>
, Linkedin <https://www.linkedin.com/in/amirsinatorfi/>
_ ]
Developers: Brendan Sherman*, James E Hopkins* [Linkedin <https://www.linkedin.com/in/jhopk>
], Zac Smith [Linkedin <https://www.linkedin.com/in/zac-smith-a7bb60185/i>
]
NOTE: This project has been developed as a capstone project offered by [CS 4624 Multimedia/ Hypertext course at Virginia Tech <https://vtechworks.lib.vt.edu/handle/10919/90655>
] and
Supervised and supported by [Machine Learning Mindset <https://machinelearningmindset.com/>
].
*: equally contributed
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}
}