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[D] Why 100 Days of ML Code Challenge is Great

I am on Day 27 today and I’m quite convinced already that consistent efforts, however small, can help someone go a long way. I’ve been wanting to actively pursue Machine Learning and Data Science for more than a year now but haven’t been consistent and usually forget after 3-4 days.

The challenge includes posting what you do on your social media handles so that you stay more committed to this challenge. After a few days, the habit sticks and you simply can’t go to sleep without learning something. I’ve had a few very busy and tiring days too in these 27 days, but I’ve made sure I did something at least in those days. I’d strongly recommend anyone who’s passionate about Machine Learning to take up this challenge.

I post my challenge details in the blog below, on Github, on Twitter, and my projects on Linkedin. https://hitheshai.blogspot.com

Here’s a summary of how much I was able to learn because of this challenge in 25 Days. Also, getting best wishes from Josh Starmer on one of my Twitter posts (a scholar who runs Statquest channel on YouTube, one of the best in the genre) was a great deal of encouragement.

(Note: In my version of this challenge, I don’t necessarily have to code everyday because of college and other commitments. Some days, even watching a single YouTube video might be sufficient as long as I make some progress from the previous day.)

Completed two MOOCs on Coursera •Machine Learning (Days 1-10) •Neural Networks and Deep Learning, Part 1 of Deep Learning Specialization (Days 20-25)

Did 2 mini-projects •Clustering (Day 4) •Anomaly Detection (Day 7)

Participated in a Kaggle competition (Boston House Price Prediction) and learnt useful tree based models and data cleaning techniques. (Days 11-18)

submitted by /u/hithesh111
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.