A Machine Learning Course with Python

Table of Contents

• Download Free Deep Learning Resource Guide
• Slack Group
• Introduction
• Motivation
• Machine Learning
  • Machine Learning Basics
  • Supervised learning
  • Unsupervised learning
  • Deep Learning
• Pull Request Process
• Final Note
• Developers
• Citation

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

Title	Code	Document
Linear Regression	Python	Tutorial
Overfitting / Underfitting	Python	Tutorial
Regularization	Python	Tutorial
Cross-Validation	Python	Tutorial

Supervised learning

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

Title	Code	Document
Clustering	Python	Tutorial
Principal Components Analysis	Python	Tutorial

Deep Learning

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
                  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}
}