I recently stumbled upon the 100 Days of ML Code initiative, which is an initiative to devote 1 hour per day to either code or study machine learning for 100 days.
I started the challenge on July 9th and am going to use it to actively either improve machine learning models at work (which I unfortunately cannot blog about), or to pursue outside personal projects. Now I will take some liberties with the term “Machine Learning” and funnel it more towards “Data Science” so it will include data mining, data cleaning, and data presentation. Most of the time the actual building of machine learning model is a small sliver of the full project so I will devote my 100 days to the “full stack” of machine learning. Throughout the initiative I will occasionally blog about my progress as time permits.
Unfortunately, given that it is summer, I have started the challenge right around some vacation time, which sometimes takes me away from electronic devices. During those time periods I will read books. Two recommendations that I am currently working my way through are:
- Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
- How Not to Be Wrong: The Power of Mathematical Thinking
Both of these are excellent books and deal with topics of data collection, statistical inference, and how to create models of the world using information that is readily available.
For more information about the initiative, I encourage you to seek out the hashtag
#100DaysOfMLCode on Twitter.