Skip to content
💬 Machine Learning Course with Python. Refer to the course page for step-by-step explanations.
Branch: master
Clone or download
Amirsina Amirsina
Amirsina and Amirsina update
Latest commit 105f5df Jun 11, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
_img update May 16, 2019
code update May 7, 2019
docs update Jun 11, 2019
.gitignore Update some of the info in the readme and site index page (#26) May 7, 2019
README.rst Update README.rst Jun 6, 2019
conf.py update Apr 14, 2019

README.rst

_img/teaser.gif
_img/subscribe.gif

A Machine Learning Course with Python

https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat https://badges.frapsoft.com/os/v2/open-source.png?v=103 https://img.shields.io/twitter/follow/machinemindset.svg?label=Follow&style=social

Table of Contents

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

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]

*: equally contributed

You can’t perform that action at this time.