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Summary

• In simple words, this project is for people interested in Machine Learning but have no idea where to start from.

• This project was built for those interested in Machine Learning. It contains explanations of common Machine Learning Concepts like Supervised Learning, Unsupervised Learning, Reinforcement Learning, Time Series, Computer Vision, NLPs etc.

• If you have any Knowledge at all of Machine Learning anything at all from knowing the right spelling of the word Machine Learning to have a good grasp of a newly introduced Model Architecture then you should know that there is someone out there who needs your expertise and it all boils down to a will you help the community or will you not. if yes then click here.

• Anything can be contributed as long as it concerns Machine Learning.

Imagine a scenario where you can learn about Machine Learning and all the big-sounding words and concepts are explained with code!

A place where you can see explanations about things like Supervised Learning, Unsupervised Learning, Natural Langage Processing, Computer Vision and also see the code for them being run and it's open-source so anyone can contribute to it.

Inspiration Behind the Project

below is a picture with the words open source

I feel a lot of people fear Machine Learning and a lot of that fear is based on unnecessary things like:

• A fear of Maths

• Lack of understanding of the complex concepts

Now don't get me wrong Maths and Complex words are a big part of Machine Learning.

The Algorithms used in Machine Learning are largely based on Maths Concepts and those complex words are the explanations for the building blocks in Machine Learning.

But that doesn't mean you need to have a degree in advanced maths and statistics before you can do Machine Learning in essence once you have a computer, internet access, a web browser you can build a machine learning model.

In simple words, this project was built for people interested in Machine Learning but have no idea where to start from.

Mission of the project

This project was built for those interested in Machine Learning. It contains explanations of common Machine Learning Concepts like Supervised Learning, Unsupervised Learning, Reinforcement Learning, Time Series, Computer Vision, NLPs etc.

The repo contains coded explanations of common concepts in Machine Learning and also gives you resources on everything Machine learning from articles, books and tutorials on how to learn python to books on machine learning and articles about quantum leaps in machine learning.

In simple words, this is a repo that was built by the Machine Learning Community for the Machine Learning Community.

Project Architecture

The project repository was built with a tree-like structure in mind.

It contains folders inside folders.

For example, in the supervised Learning folder, you will find classification and regression folders (classification and regression are subsets or components of Supervised learning).

Each Folder contains a .txt file called "introduction to folder" which talks about the folder and all the stuff contained in that folder.

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Why Should You Contribute

I will keep this simple, If you have any Knowledge at all of Machine Learning anything at all from knowing the right spelling of the word Machine Learning to have a good grasp of a newly introduced Model Architecture then you should know that there is someone out there who needs your expertise and it all boils down to a will you help the community or will you not. if yes then click here.

Contribution Guidelines

• Fork the repo

• Open your terminal and clone it on your machine:

git clone https://github.com/EdemGold/Nutshell-Machine-Learning.git

• Add an upstream link to the main branch in your cloned repo

git remote add upstream https://github.com/EdemGold/Nutshell-Machine-Learning.git

• Keep your cloned repo up to date by pulling from upstream (to avoid any merge conflicts while committing new changes)

git pull upstream main https://github.com/EdemGold/Nutshell-Machine-Learning.git

• Create your own branch: git checkout -b

• Commit all the changes (use descriptive commit messages): git commit -m "a descriptive commit message"

• Push your changes: git push origin

• Make a PR on Github; use a descriptive PR title to specify the change(s) made.

• Any contribution is welcomed such as fixing grammatical errors, fixing broken links, adding resources, etc.

• Before contributing, here's our CODE OF CONDUCT which must be adhered to.

• Verify that any resource(s) to be added isn't already present in the repo, as yours may be a duplicate.

• Use the Title Case Name format.

• Use concise descriptions.

• Pull requests should have a useful title.

• To add any Machine Learning concept, create a folder in the ML Concepts folder and add the concept to be added with detailed explanations. Also, if there are packages to be installed, create a .txt file stating out the packages to be installed and how to install these packages.

• Codes can be contributed using jupyter notebooks and .py, .r or .jl files. But jupyter notebooks will be much appreciated because they support a clear explanation of code.

What can you Contribute

Anything can be contributed as long as it concerns Machine Learning.

If you can't contribute code them you can help fix a typo just make sure you contribute where it's needed.

It's our responsibility as a community to help each other. Knock yourself out.

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