GitHub - llSourcell/100_Days_of_ML_Code: These are the instructions for "10...

 4 years ago
source link: https://github.com/llSourcell/100_Days_of_ML_Code
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These are the instructions for this video on Youtube by Siraj Raval for the #100DaysofMLCode Challenge.


Machine Learning is the most transformative technology of our time. Whether its helping us discover new drugs for major diseases, fighting fraud, generating music, improving supply chain efficiency, the list of applications are truly endless. In order for us as a community to be able to make valuable contributions to the world, we need to master this technology. This is a call to action, a battle cry, a spark that will light a movement to radically improve the state of humanity. 100 Days of ML Code is a committment to better your understanding of this powerful tool by dedicating at least 1 hour of your time everyday to studying and/or coding machine learning for 100 days.


  • Everyone is eligible, even people who've never coded before

The 3 Rules

  • Make a public pledge to code or study machine learning for minimum 1 hour every day for the next 100 days via your favorite social platform using the #100DaysofMLCode Hashtag.
  • Make a public log of your work. Update it daily. Here is a GitHub example template. Another one is here. You can also make a blog or vlog.
  • If you see someone make a post using the #100DaysofMLCode hashtag, encourage them via a 'like', 'share', or comment!

Project Ideas

  • Siraj's pick is Preventing the Spread of Dengue Fever. The task is to predict the number of dengue cases each week (in each location) based on environmental variables describing changes in temperature, precipitation, vegetation, and more. An understanding of the relationship between climate and dengue dynamics can improve research initiatives and resource allocation to help fight life-threatening pandemics. You can literally save lives with machine learning.

  • There are many other projects out there though. Pick an industry that excites you, find a problem they have, locate a relevant dataset, apply AI to that dataset, and monetize the solution.

Learning Resources


  • Siraj Raval will give a shoutout to some of the Wizards who succesfully complete the challenge, notify him if you've completed it via Twitter @sirajraval
  • By the end of 100 days, you'll be expected to have a project you've contributed to, whether your own or another. The more impact you've had, the more likely you'll get a shoutout. Impact comes in the form of good documentation of your journey so that others can follow, a project that improves the lives of other people, or real progress in your own ability to code machine learning.

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