The last one is actually the OP it seems, and each video is a good chunk apart in time posted. Not sure what to make of it, I just don't like it when a title like this is the exact same but is made to seem like it was genuinely written by the person who posted it.
For me that's a natural evolution and I find it hard to accept that in certain fields you are supposed to get in contact with the academical background first before jumping in to actual usage. It's like having to understand the detailed physics of an internal combustion engine before driving or before repairing the car.
A big part of this this knowing how best to fit a model to the data, which usually requires knowledge of mathematical obscurities to avoid things like overfitting, local optima, etc. It's not glamorous stuff though, so it usually isn't brought up in presentations like this one.
My early coding years were spent typing in BASIC game programs from magazines (wumpus hunters represent!), tweaking them, and later making up my own. There was a lot of theory that I could have benefited from, but I never had the motivation to learn it until later, when learning the theory solved problems I had actually experienced.
But here we're talking about a series of intro videos and the appropriate pedagogical approach. It really could be that ML has more subtle failure modes than programming, although I'm suspicious; I remember a lot of my novice C issues where the program did happen to appear to work, at least for short periods, even though my code was terrible. But if it is, I think the trick there isn't to prescribe a heavier dose of theory, it's to get people to experience problems like you describe in a way where they can quickly detect and learn from them.
Yes, we need to quantify tradeoffs between models mathematically, but that does not not require knowledge of the mathematics behind the models themselves. With cross validation, I can estimate the effectiveness of many black box models, without looking inside them. This step is called error estimation, and comes before model selection.
I can arrive at a pretty good model by a combination of correct methodology and brute force. It is this methodology that makes up much more of the overall picture. You could give me a black box, a rough range of parameters it takes, and I can tell you how likely it is to work well. This approach doesn't scale well to bigger problems, but I doubt tackling Big Data problems is the intention behind this course.
Tuning parameters and selecting features needs (a) understanding of the model(s) used and (b) an understanding of the data.
'Brute forcing' these steps can grow exponentially in time (eg. feature selection out of n features takes 2^n combinations) and makes your approach not only very inefficient but also doesn't predict if you have a good model. Your approach makes sensitivity analysis makes very very hard.
Don't worry, as with anything there's a certain subset of people who actually know the underlying principles behind a subject, and for some reason feel threatened when those principles are abstracted away, as if their knowledge is now wasted. But that's the natural progression of things. Sorry.
It's funny it happens in a community of programmers though, where half of the tools that are used everyday are blackboxes that few really understand. Like the computer itself.
For example deep-learning really revolutionized the state of the art in image recognition in 2012 by winning academic competitions. It took about 3-5 years for those deep learning algorithms to get productized into packages like tensorflow, with high production tutorials and videos, so it was accessible to non-academics.
I don't think people that know the underlying principles of machine learning are threatened (Thats sounds like pretty insecure world view on your part). They operate in a different context where you want to push the state of the art in machine learning algorithms, instead of just applying existing best-practices to your specific problem.
I agree with your post, but 99.9% of people who will be applying ML via black-box algorithm in the next decade won't be participating in, or at all concerned with, the state-of-the-art. In the same way that most of us aren't concerned about state-of-the-art chip design.
I can do a regression analysis with a couple clicks in excel. I need little knowledge beyond how to interpret results. Sure, the underlying data might violate some assumptions, but it's rare (and there are tools for that). And let's face it, the most popular applications by amateurs will be marketing related, not cancer-curing related.
I have a degree in stats and someone at work who is self taught from a 'use the tools' perspective was trying to use these frameworks to analyse some log file patterns. When I had a look at it, his results were showing that they were statistically significant, but the data didn't look anything like a linear relationship and fitting it to a regression wasn't a valid move. That's a simplistic example but even in the relatively simple realm of linear regression there are more difficult traps to spot, like heterostedasticity or error normality.
But nothing you've said is complicated enough that in can't be explained through simple instructions or conquered through better tools. This is besides the fact that a little bias in the estimation isn't the end of the world if you're only trying to figure out who clicks ads, and not doing medical research.
Believe me, I run into the same issues as well, having to state "You can't do that..." when I watch co-workers try to apply even simple tests. I just think we draw the cut-off line at different skill-levels.
Basically: "instructions", that become more simple over time. There are some nuances to, say, R^2. But the concept that it's "how much variance is explained by the model" isn't difficult to comprehend...or apply.
Let me clarify that I'm not saying it's unimportant to understand the underlying mathematics behind these processes. After all, someone has to design these things so that the layman can actually apply them. What I, and it seems others, are arguing is that it isn't necessary to have a deep understanding of the algorithms to get insight from their usage. Some creative person creates the tool, and other creative people figure out its best uses. They are rarely the same people.
I'll add: I'm not sure why you're down-voted. This community seems to be developing those bad habits of disagree = down vote.
Computers are like cities, they can be managed effectively only by dealing with aggregates and abstractions. It's impossible for someone to know every tile in the sidewalk, but it is possible for them to effectively manage sidewalk repair if they have the right abstractions.
There are a lot of people who have been forward thinking enough to see the value and incorporate this into their ideas, but there are so many more who think it's a magical black box that has no relevancy to their world.
While some might feel like this is too simple, I think this kind of basic introduction is very powerful.
I have written my hello world algorithm, and I'm very happy about it =)
I have been meaning to start learning this stuff for a long time, but it's really hard, most of the resources are above the level I'm currently at.
This is so amazing to see an accessible course like this, I really hope they will keep it up. It would be a really fun thing to focus on it during this summer.
You can also probably setup ifttt to send you an email when the new feed item appears.