Contents

Machine learning Knowledge Project¶

  • Use this project to make your first contribution to an open-source project on GitHub. Practice making your first pull request to a public repository before doing the real thing!

  • Celebrate Hacktoberfest by getting involved in the open-source community by completing some simple tasks in this project.

  • This repository is open to all members of the GitHub community. Any member may contribute to this project without being a collaborator.

  • https://github.com/Vatsalparsaniya/ML_Knowledge

What is Hacktoberfest?¶

  • A month-long celebration from October 1st - 31st sponsored by Digital Ocean and GitHub to get people involved in Open Source. Create your very first pull request to any public repository on GitHub and contribute to the open source developer community. https://hacktoberfest.digitalocean.com/

How to contribute to this project😕¶

  • Here are quick and painless ways to contribute to this project:

  • Add your name to the Contributors.md file

    • Ex. [Vatsal Parsaniya](https://github.com/Vatsalparsaniya)

  • Collect Machine Learning, Deep Learning, DataScience Concepts or Algorithm if the information is taken form any reference don’t forget to add a reference.

    • Ex. I want to Add Concepts of Outlier

      • make a folder named Outlier, if already present in repository add additional information into that.

      • create Readme.md file in Outlier folder add your knowledge there

      • You can add knowledge like:

        • Why this concept

        • How this algorithm works

        • Advantages

        • Disadvantages

        • What Are the Basic Assumption

        • In which problem statement you can use this concept or Algorithm

      • list of concepts: Stacking, Ensembling, Cross-Validation, Stacking VS Blending, standardisation, normalization, standardisation vs normalization, etc…

      • List of Algorithms: Linear Regression, Logistic Regression, Decision Tree, SVM (Support Vector Machine), Naive Bayes, KNN (K- Nearest Neighbors), K-Means, Random Forest, etc…

  • Choose one or all, make a pull request for your work and wait for it to be merged!

Getting started¶

  1. Fork this repository (Click the Fork button in the top right of this page, click your Profile Image)

  2. Clone your fork down to your local machine

https://github.com/Vatsalparsaniya/ML_Knowledge.git

  1. Create a branch

git checkout -b branch-name

  1. Make your changes (choose from any task listed above)

  2. Commit and push

git add
git commit -m 'Commit message'
git push origin branch-name

  1. Create a new pull request from your forked repository (Click the New Pull Request button located at the top of your repo)

  2. Wait for your PR review and merge approval!

  3. Star🌟 this repository if you had fun!

Contributors