Machine Learning Course with Python:

Last update: Jan 03, 2023

Overview

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

Download Free Deep Learning Resource Guide
Slack Group
Introduction
Motivation
Machine Learning
Pull Request Process
Final Note
Developers
Citation

Download Free Deep Learning Resource Guide

Introduction

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

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:

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

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}
}

Comments

OF and LR updates

Taking into account review notes. Having trouble setting up my python environment, so I have not been able to test the code yet. I hope to fix that today/tomorrow. Fixed the table in LR.

opened by BroccoliHijinx 11
Multilayer Perceptron write-up

Submitting a PR now to allow for comments on what is done. There are placeholders for what is left to be done, and I should be able to do that tomorrow.

Left to do;

images and associated text

More on backprop

Defining and explaining actual MLPs (most right now is on NN basics)

opened by BroccoliHijinx 3
Addressed comments brought up in peer review

I decided to remove the multiple linear regression section because it seems beyond the scope of this module. Those images, MLR.png and MLR_POBF.png, can safely be removed from our image folder. I left a mention to it for completeness. I also added captions for all figures and equations to explain what they are.

opened by b-sherman 3
Logistic Regression Files, some overfitting changes

Within Logistic Regression, I have a table that I cannot get working. I want to keep messing around with it, but I'm not sure what is wrong. I am using the rst basic table, but I think the spacing is off somehow.

opened by BroccoliHijinx 3
Naive bayes question

Hi @astorfi , Thanks for your great work ! I'm a beginner of ML. Tonight when I learn Naive Bayes Classification in your tutorial, I found the Equation 1 in the tutorial is different from that in Wiki. I wonder which one is correct or both of them are right?

Look forward to your reply.

opened by suedroplet 2
Chinese Translation

Hi @astorfi , Thanks for your great work ! My friends and I have learned a lot here. China has a platform called KESCI (https://www.kesci.com). They provide algorithm competition opportunities for developers, which is similar to Kaggle, and self - training online environment to enhance their algorithmic ability. I am going to translate the whole series to Chinese and applied for a column to publish them on KESCI, as a series. Hope to get your permission. thanks.

opened by Vivian0210 2
Overfitting rst file

I don't think including code with this module makes much sense, so I just included a write-up. I tried to keep it short and simple, since this is something to keep in mind in the entire course.

opened by BroccoliHijinx 2
Naive bayes

I just created a new branch for the updated naive bayes files since the old one is very far behind now. Included are the images, code, and module text.

opened by b-sherman 1
Linear regression

I redid all the linear regression code with a completely new data set to assure originality and because the existing scikit-learn ones are confusing to me so they are bound to be confusing to a new reader. I also changed all the images to reflect the new code. I tried to simplify the code as much as possible and only used the bare minimum number of references to scikit-learn functions. I also revised the rst document to reflect these changes. All generated images now have a link to the code I used to create them as well because it seemed like a good idea.

opened by b-sherman 1
Updated linear_regression.rst

+Added a Motivation section that talks about what the problem is +Changed raw URLs into hyperlinks on smaller words +Added a Code section that links to the module code and talks about what it does +Added a Conclusion section to close out the module

opened by b-sherman 1
Reference fixes

Changed the "References" indent level in several modules to be consistent. Changed header casing in some modules to be consistent. Requesting merge so that the site can be updated for screenshots to include in the final project report.

opened by b-sherman 0

Releases(1.0)

1.0(Dec 19, 2019)

Source code(tar.gz)
Source code(zip)

Owner

Instill AI

A company offering AI-based solutions to real-world applications.

GitHub Repository https://machine-learning-course.readthedocs.io/en/latest/

Machine Learning from Scratch

Machine Learning from Scratch Author: Shengxuan Wang From: Oregon State University Content: Building Machine Learning model from Scratch, without usin

0 Jul 05, 2022

ThunderSVM: A Fast SVM Library on GPUs and CPUs

What's new We have recently released ThunderGBM, a fast GBDT and Random Forest library on GPUs. add scikit-learn interface, see here Overview The miss

1.4k Dec 22, 2022

A python library for Bayesian time series modeling

PyDLM Welcome to pydlm, a flexible time series modeling library for python. This library is based on the Bayesian dynamic linear model (Harrison and W

438 Dec 17, 2022

Machine-Learning with python (jupyter)

Machine-Learning with python (jupyter) 머신러닝 야학 작심 10일과 쥬피터 노트북 기반 데이터 사이언스 시작 들어가기전 https://nbviewer.org/ 페이지를 통해서 쥬피터 노트북 내용을 볼 수 있다. 위 페이지에서 현재 레포 기

1 Jan 23, 2022

As we all know the BGMI Loot Crate comes with so many resources for the gamers, this ML Crate will be the hub of various ML projects which will be the resources for the ML enthusiasts! Open Source Program: SWOC 2021 and JWOC 2022.

Machine Learning Loot Crate 💻 🧰 🔴 Welcome contributors! As we all know the BGMI Loot Crate comes with so many resources for the gamers, this ML Cra

89 Dec 28, 2022

Both social media sentiment and stock market data are crucial for stock price prediction

Relating-Social-Media-to-Stock-Movement-Public - We explore the application of Machine Learning for predicting the return of the stock by using the information of stock returns. A trading strategy ba

15 Oct 29, 2022

Hypernets: A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

216 Dec 23, 2022

onelearn: Online learning in Python

onelearn: Online learning in Python Documentation | Reproduce experiments | onelearn stands for ONE-shot LEARNning. It is a small python package for o