This is the most approachable, comprehensive machine learning content I've seen. The analogies are powerful, useful, and applicable. I also appreciated your emphasis on the important role played by cross-functional teams of human decision-makers and qualified 'Wish Makers.' The human element, I've found, is often overlooked and unappreciated. Thanks for sharing!
It is true that the terminology can be really confusing whereas the actual thing it refers to could be simply explained even to a child. Thank you for the great material!
This is really great content and I am truly inspired by it! Are any notes, blog post or slide deck available for quickly referencing the content for future use?
28:01 : Most imp first step 37:42 : Did it get it right or not?, did it found a cat? Precision vs recall is very important 1:21:04 : It is most important line for Applied ML. How long does each one take to try.
George Babbage was asked by an MP (funding his Difference Engine) "If the machine is given the wrong numbers, will it return the right answer?". By the sound of it, decision-makers haven't improved over a couple of centuries.
Cat, or not cat, and the tiger. Now, think about how we get intuition about numbers. 5 digits of the hand, 3 the leaf clover, 2 wings of the bird...the 5 can be saw on 5 cats, the 3 in three units of cats, the 2 on two cats and so on...what I mean? About generalization...this power to generalize, acquire something that is a pattern among the things, we, humans, can do trough some examples...the machine can do from more examples (data)...what are the patterns that the AI had discovered and that we, humans, can not see? Some examples?
