Machine Learning Course

💬 Machine Learning Course with Python:
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A Machine Learning Course with Python

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Table of Contents

Download Free Deep Learning Resource Guide

Slack Group

Introduction

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

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

Machine Learning Basics

_img/intro.png
Title Code Document
Linear Regression Python Tutorial
Overfitting / Underfitting Python Tutorial
Regularization Python Tutorial
Cross-Validation Python Tutorial

Supervised learning

_img/supervised.gif
Title Code Document
Decision Trees Python Tutorial
K-Nearest Neighbors Python Tutorial
Naive Bayes Python Tutorial
Logistic Regression Python Tutorial
Support Vector Machines Python Tutorial

Unsupervised learning

_img/unsupervised.gif
Title Code Document
Clustering Python Tutorial
Principal Components Analysis Python Tutorial

Deep Learning

_img/deeplearning.png
Title Code Document
Neural Networks Overview Python Tutorial
Convolutional Neural Networks Python Tutorial
Autoencoders Python Tutorial
Recurrent Neural Networks Python IPython

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, GitHub, Twitter]

Supervisor: Amirsina Torfi [GitHub, Personal Website, Linkedin ]

Developers: Brendan Sherman*, James E Hopkins* [Linkedin], Zac Smith [Linkedin]

NOTE: This project has been developed as a capstone project offered by [CS 4624 Multimedia/ Hypertext course at Virginia Tech] and Supervised and supported by [Machine Learning Mindset].

*: equally contributed

Citation

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

@software{amirsina_torfi_2019_3585763,
  author       = {Amirsina Torfi and
                  Brendan Sherman and
                  Jay Hopkins and
                  Eric Wynn and
                  hokie45 and
                  Frederik De Bleser and
                   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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