We just recently stumbled upon a mini-series from R2D3 (http://www.r2d3.us/), explaining machine learning with visuals instead of equations. It’s describing one of the most common introductory topics to machine learning: housing predictions.
Say you need to determine whether something belongs to either of two groups. How would you do it?
What happens when we go from examining a single property to multiple?
Is it possible to become too good at something, effectively resulting in worse decision-making?
Answers to these questions (and more) are beautifully illustrated here:
(http://www.r2d3.us/visual-intro-to-machine-learning-part-1/), and they also have a second part up if you’re keen on more.
Click here to check it out