The quality of mercy is not strained;
It droppeth as the gentle rain from heaven
Upon the place beneath.
It is twice blest; It blesseth him that gives and him that takes:
‘Tis mightiest in the mightiest; it becomes
The throned monarch better than his crown:
His sceptre shows the force of temporal power,
The attribute to awe and majesty,
Wherein doth sit the dread and fear of kings;
But mercy is above this sceptred sway;
It is enthronèd in the hearts of kings,
It is an attribute to God himself;
William Shakespeare, The Merchant of Venice, Act IV, Scene I
The key word in data science is science, not data.
Leek, Caffo, Peng (Executive Data Science on Coursera)
The master has failed more times than the beginner has even tried.
Two definitions of Machine Learning
Arthur Samuels in 1959 described machine learning as the field of study that gives computers the ability to learn without being explicitly programmed.
Tom Mitchell of Carnegie Mellon, in 1998, described machine learning as a “well-posed learning problem,” in which a computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.
Source: Andrew Ng, Machine Learning class on Coursera
If you want to get to the source, you must swim against the current.
Two questions I find useful, when making decisions:
What am I optimizing for?