A Machine Learning Course with Python
Table of Contents
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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:
- 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}
}