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      Hacktoberfest Contributor’s Guide: How To Find and Contribute to Open-Source Projects


      Introduction

      Contributing to open source software is not only a way to share your skill in a particular language or tech stack, it can be a rewarding practice to share your engineering knowledge and collaborate with the developer community. Although there’s a wide range of open source projects out there waiting for your expertise, knowing where to find them and how to contribute in a way that is meaningful to the project can sometimes prove to be a barrier for interested contributors.

      In this Hacktoberfest-flavored guide, we’ll share some tips and information that will aid in finding and contributing meaningfully to open source projects.

      Find a Project

      If you are new to engaging with the open source community, finding a new project to contribute to may feel daunting. Here’s a few resources and ideas to help you find a project you’d love to help thrive.

      What is Open Source?

      Open source software is software that’s freely available to use and modify, typically shared via a public repository hosting service like Github. Projects that follow the open source model usually thrive through contributions from the developer community, and may allow for redistribution depending on which open source license they have adopted.

      Most successful open source projects have transparent, well-delineated processes for maintenance and improvement, which helps to build a community around them. As a result, they benefit from regular contributions from end-users, who bring with them diverse perspectives to solutions that may otherwise be overlooked.

      To learn more in detail about open source, visit our tutorial series, An Introduction to Open Source.

      Consider Familiar Open Source Software

      After deciding to commit your time and talent to an open source project, it’s important to take a moment to consider your passions and the type of project that resonates with you. Considering that you may spend a number of hours contributing to a specific project, you want to select a project that is not only something you’d personally use, but have a deeper interest in beyond contributing for Hacktoberfest. Think about the software you use today and consider the following:

      • In what tech stack and language is the software written?
      • What are some things that could be improved when using the software?
      • Are there any bugs or visible errors that you have the technical proficiency to address?
      • Would you be willing to contribute to this software on an ongoing basis?

      These beginning considerations may lead you to discover that your favorite software is open source and waiting for your contribution. If that’s the case, be sure to dive into the CONTRIBUTING.MD file that typically delineates how to contribute before starting. This resource will usually introduce you to the codebase, conventions, and ways to gain support when contributing to the software.

      Beginner-Friendly Open Source Projects to Try

      If you’re just starting out, the idea of committing large amounts of code to an unfamiliar codebase could bring out the imposter syndrome that lies dormant in many of us. Luckily, each developer was a beginner once, and to foster appreciation and adoption of open source, there’s a wealth of publicly-available repositories shared by fellow developers that are beginner-friendly. Here’s a few that we suggest to browse:

      • Awesome For Beginners- A list of projects by programming language that are noted to be beginner-friendly.
      • Awesome for Non-Programmers- if you’re new to programming, here’s a list of projects that are language-agnostic and help foster learning.
      • Up For Grabs- A resource that lists projects with tasks curated for new contributors.
      • First Timers Only- A resource for beginning contributors that includes links to open source learning resources and links to beginner-friendly projects.
      • Habitica- A habit-forming app that gamifies life. This open source project has detailed documentation and many ways for programmers and non-programmers alike to contribute to the project.

      More resources for open source projects to try can be found on our Hacktoberfest Resources Page.

      Make a Contribution

      Identifying Meaningful Solutions for Open Source Projects

      After identifying an open source project to contribute to and diving into the resource material that the codebase offers, you may be wondering exactly what to contribute. While the way in which you contribute may vary by project, here’s some general ideas of contributions that are impactful and meaningful to the codebase and software you’re working on.

      Fix a Bug

      Bugs are small errors in code that may cause an annoyance, a blocker, or be debilitating to software. Bugs often produce unexpected results that cause incorrect responses or actions — for the sake of a software user’s experience, it’s imperative and important that a codebase is maintained to be bug-free (or as bug-free as possible).

      You can contribute your knowledge and expertise to ‘squash’ or solve the issue surrounding a bug. By working on bugs of varying priorities, your ability to strengthen a codebase by solving errors will grow, and you’ll have a meaningful contribution to add.

      Propose a Feature

      Open source projects benefit from a diversity of thought. Although software may have been developed by one or more engineers with an opinion of how their product can solve an existing problem, your personal experience and outlook on how to improve a project can be invaluable. Once you’re comfortable with a project’s codebase and understand how it works for end users, try to think of a new feature that could be useful or improve the user’s experience and create an issue to propose it to the project maintainers. It is important to have this conversation before investing time in writing code, since sometimes your idea might not coincide with the project’s roadmap. With a positive response, it’s time to implement your idea and bring that feature to production.

      Write Some Documentation

      While there may be a wealth of technical contributions that can be made to a codebase, writing good documentation is a contribution that is often overlooked. If you’re linguistically-inclined or speak a language other than the one reflected in the initial documentation, consider making a contribution. Contributions in documentation can revolve around providing editing help to an existing doc or authoring new pages within the documentation. Refer to your project’s contribution guidelines to learn more about how to contribute this and other non-technical help.

      Submit Your PR

      Submitting Your Pull Request via Github

      After you’ve made a meaningful contribution to an open source project’s codebase, it’s time to submit your pull request. We’ve created a helpful video that walks you through this process via Github, that can be found here.

      Video: How to Submit Your First PR

      Sharing your expertise with an open source project is a rewarding experience that allows you to practice your talent, collaborate with and learn from others, and give back to the developer community. While it may initially seem daunting to find your place within the open source community, finding a project that speaks to your passions and contributing meaningfully to its codebase is a great way to start.

      For Hacktoberfest, while making four (4) meaningful contributions to open source projects will qualify you for prizes, we hope that you’ll continue to enjoy the benefits of contributing to the open source community well beyond the event. For more information or to learn more about open source, Git, or Github, you can visit the Hacktoberfest resources page. Happy hacking!



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      Python Machine Learning Projects — A DigitalOcean eBook


      Download the Complete eBook!

      Machine Learning Projects: Python eBook in EPUB format

      Machine Learning Projects: Python eBook in PDF format

      Machine Learning Projects: Python eBook in Mobi format

      Introduction to the eBook

      As machine learning is increasingly leveraged to find patterns, conduct analysis, and make decisions — sometimes without final input from humans who may be impacted by these findings — it is crucial to invest in bringing more stakeholders into the fold. This book of Python projects in machine learning tries to do just that: to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning to help ensure that it is serving us all.

      This book will set you up with a Python programming environment if you don’t have one already, then provide you with a conceptual understanding of machine learning in the chapter “An Introduction to Machine Learning.” What follows next are three Python machine learning projects. They will help you create a machine learning classifier, build a neural network to recognize handwritten digits, and give you a background in deep reinforcement learning through building a bot for Atari.

      These chapters originally appeared as articles on DigitalOcean Community, written by members of the international software developer community. If you are interested in contributing to this knowledge base, consider proposing a tutorial to the Write for DOnations program. DigitalOcean offers payment to authors and provides a matching donation to tech-focused nonprofits.

      Other Books in this Series

      If you are learning Python or are looking for reference material, you can download our free Python eBook, How To Code in Python 3

      For other programming languages and DevOps engineering articles, check out our knowledge base of over 2,100 tutorials.

      Download the eBook

      You can download the eBook in either the EPUB, PDF, or Mobi format by following the links below.

      Download the Complete eBook!

      Machine Learning Projects: Python eBook in EPUB format

      Machine Learning Projects: Python eBook in PDF format

      Machine Learning Projects: Python eBook in Mobi format



      Source link

      Machine Learning Projects: Python — A DigitalOcean eBook


      Download the Complete eBook!

      Machine Learning Projects: Python eBook in EPUB format

      Machine Learning Projects: Python eBook in PDF format

      Machine Learning Projects: Python eBook in Mobi format

      Introduction to the eBook

      As machine learning is increasingly leveraged to find patterns, conduct analysis, and make decisions — sometimes without final input from humans who may be impacted by these findings — it is crucial to invest in bringing more stakeholders into the fold. This book of Python projects in machine learning tries to do just that: to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning to help ensure that it is serving us all.

      This book will set you up with a Python programming environment if you don’t have one already, then provide you with a conceptual understanding of machine learning in the chapter “An Introduction to Machine Learning.” What follows next are three Python machine learning projects. They will help you create a machine learning classifier, build a neural network to recognize handwritten digits, and give you a background in deep reinforcement learning through building a bot for Atari.

      These chapters originally appeared as articles on DigitalOcean Community, written by members of the international software developer community. If you are interested in contributing to this knowledge base, consider proposing a tutorial to the Write for DOnations program. DigitalOcean offers payment to authors and provides a matching donation to tech-focused nonprofits.

      Other Books in this Series

      If you are learning Python or are looking for reference material, you can download our free Python eBook, How To Code in Python 3

      For other programming languages and DevOps engineering articles, check out our knowledge base of over 2,100 tutorials.

      Download the eBook

      You can download the eBook in either the EPUB, PDF, or Mobi format by following the links below.

      Download the Complete eBook!

      Machine Learning Projects: Python eBook in EPUB format

      Machine Learning Projects: Python eBook in PDF format

      Machine Learning Projects: Python eBook in Mobi format



      Source link