instillai/machine-learning-course
Là một công cụ AI, instillai/machine-learning-course đã đạt 7.0k sao trên GitHub, ngôn ngữ Python. :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: 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_>_ | Tutorial <lrtutorial_>_ |
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
| Overfitting / Underfitting | Python <overcode_>_ | Tutorial <overtutorial_>_ |
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
| Regularization | Python <regcode_>_ | Tutorial <regtutorial_>_ |
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
| Cross-Validation | Python <crosscode_>_ | Tutorial <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_>_ | 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_>_ |
+--------------------------------------------------------------------+-------------------------------+------------------------------+
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_>_ | Tutorial <clusteringdoc_>_ |
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
| Principal Components Analysis | Python <pcacode_>_ | Tutorial <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_>_ | Tutorial <mlpdoc_>_ |
+--------------------------------------------------------------------+-------------------------------+---------------------------+
| Convolutional Neural Networks | Python <cnncode_>_ | Tutorial <cnndoc_>_ |
+--------------------------------------------------------------------+-------------------------------+---------------------------+
| Autoencoders | Python <aecode_>_ | Tutorial <aedoc_>_ |
+--------------------------------------------------------------------+-------------------------------+---------------------------+
| Recurrent Neural Networks | Python <rnncode_>_ | IPython <rnndoc_>_ |
+--------------------------------------------------------------------+-------------------------------+---------------------------+
======================== Pull Request Process
Please consider the following criterions in order to help us in a better way:
- The pull request is mainly expected to be a link suggestion.
- Please make sure your suggested resources are not obsolete or broken.
- Ensure any install or build dependencies are removed before the end of the layer when doing a build and creating a pull request.
- Add comments with details of changes to the interface, this includes new environment variables, exposed ports, useful file locations and container parameters.
- 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
Supervisor and creator of the project: Amirsina Torfi [GitHub <https://github.com/astorfi>, Personal Website <https://astorfi.github.io/>, Linkedin <https://www.linkedin.com/in/sinalk/>_ ]
Developers: Amirsina Torfi, 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
====================== 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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Machine Learning for Beginners - A Curriculum is a free curriculum published at microsoft/ML-For-Beginners by Microsoft's Cloud Advocates team. It runs 12 weeks across 26 lessons and 52 quizzes, teaching classic machine learning with Scikit-learn while pointing deep learning topics to a separate Microsoft curriculum. The repository is MIT-licensed and has been translated into more than 50 languages through an automated GitHub Action.
TensorFlow is Google's open-source, end-to-end platform for machine learning, hosted at tensorflow/tensorflow under the Apache-2.0 license. It was originally built within Google Brain's Machine Intelligence team for ML and neural network research, and today it ships stable Python and C++ APIs alongside GPU, CPU-only, and Docker install paths. The README positions it as covering both research work and shipping ML-powered applications.
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Trả lời nhanh
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Dự án có trang chủ ở https://machine-learning-course.readthedocs.io/en/latest/. Tab "Readme" ở trang này thường có ảnh chụp và hướng dẫn bắt đầu nhanh.
instillai/machine-learning-course là gì?
instillai/machine-learning-course (instillai/machine-learning-course) là dự án Python trên GitHub. Theo mô tả gốc: :speech_balloon: Machine Learning Course with Python:
Vì sao instillai/machine-learning-course được xếp vào nhóm AI Tools?
TopGit xếp instillai/machine-learning-course vào nhóm AI Tools dựa trên GitHub topics và mô tả của repo (gắn thẻ: "algorithms", "artificial-intelligence", "machine-learning"). Việc phân loại dựa trên metadata thật của repo, không phải đoán theo cảm tính biên tập.
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