
Google has started a video series on machine learning and I can understand it - iamkeyur
https://www.youtube.com/watch?v=cKxRvEZd3Mw
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acconrad
The videos are neat but kind of minimal. Is it odd for me to feel
uncomfortable and skeptical of this series because of the title? Google has
clearly figured out how to market this video, since the "I" changes but the
message is the same every time:

[https://www.reddit.com/r/programming/comments/4eyyhm/google_...](https://www.reddit.com/r/programming/comments/4eyyhm/google_has_started_a_new_video_series_teaching/)

[https://www.reddit.com/r/coding/comments/4f1p35/google_has_s...](https://www.reddit.com/r/coding/comments/4f1p35/google_has_started_a_video_series_on_machine/)

[https://www.reddit.com/r/MachineLearning/comments/4f07rp/goo...](https://www.reddit.com/r/MachineLearning/comments/4f07rp/google_has_started_a_new_video_series_teaching/)

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.

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tlrobinson
It's really common for Redditors to cross-post other peoples' submissions to
other subreddits without changing the title.

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IvanK_net
Keep in mind, that these videos don't teach you any machine learning. They
teach you how to use programs, which perform machine learning for you.

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splike
The mathematics of the model you end up using at the end of the day is such a
small part of the overall picture when making use of machine learning.

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machinelearning
This is so false. Applying machine learning to a real world problem requires
correct intuition and the ability to quantify tradeoffs mathematically. This
is developed by understanding the math behind the model and what the tradeoffs
are.

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splike
I completely disagree.

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.

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forgetsusername
> _I completely disagree_

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.

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flipgimble
And its completely fine to be the developer who uses pre-made algorithmic
block for their specific problem. However you will always be several years
behind the current state of art.

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.

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forgetsusername
> _However you will always be several years behind the current state of art_

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.

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njwi332
Actually a regression analysis is a great example of something people often
use incorrectly.

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.

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forgetsusername
> _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.

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Gratsby
I think they've done a good job of simplifying things to the point where the
viewer feels not only that ML is useful, but it's achievable.

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.

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rayalez
Amazing video, can't wait for the next one!

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.

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ea016
For those interested, episode 2 is out [0]. There is also a playlist
containing all episodes [1]

[0]
[https://www.youtube.com/watch?v=tNa99PG8hR8](https://www.youtube.com/watch?v=tNa99PG8hR8)
[1]
[https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6r...](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal)

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simonw
Dumb YouTube question: is it possible to subscribe to a playlist do I get a
push notification when new videos are added to it? I don't particularly want
to subscribe to the entire high traffic Google Developers channel.

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rayalez
Nope, but you can subscribe to the RSS feed(just paste the playlist url in
feedly).

You can also probably setup ifttt to send you an email when the new feed item
appears.

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coderKen
Thanks for sharing this, been planning to get into Machine learning this year.

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21
Python 2.7 :)

