okay so the other day I hosted a meet-up
right here in Toronto and in this meetup
we had a Q&A session with machine
learning engineers from this company
called DISA it's a company that
implements machine learning solutions
for other companies including a bank and
I thought this Q&A session was really
informative but it was pretty long it
was about 40 minutes long in total and
the audio we got from the event wasn't
the best I would say so I decided to
give you a summarized version of the Q&A
session in this video but just in case
you want to watch the entire raw footage
of this Q&A I'm gonna put a link to that
in the description and if you want to
sign up for future meetups in Toronto
and maybe elsewhere I'm gonna put a link
to sign up for that in the description
below - okay so this Q&A session was
with machine learning engineers Joe mark
Helen Pippin Danny and Sachin and the
first question I asked was what is
machine learning exactly this is
actually something I already talked
about on this channel in my video about
what can you do with Python but let me
summarize their explanation here so
machine learning is used when you can't
explicitly write on the algorithm to
solve a given problem an example of a
problem like this is when you want to
make a computer categorize images so
let's say that you want your computer to
understand that this picture contains a
dog and this other picture contains a
car you can solve this problem with
machine learning by giving a machine
learning algorithm a bunch of labeled
examples and then a your machine
learning algorithm which might be for
example a neural network will learn to
label new images into either dogs or
course so that's basically what machine
learning does it's a way for a computer
to learn patterns from a bunch of
examples anyway after that I asked them
if they could share some interesting
applications of machine learning
one application Helen right there talked
about is how some people have been
trying to use machine learning to
generate things like art poetry and
music so I asked them how you would
actually go about doing that and then
Joe right there explained that for the
example of poetry you can just take a
bunch of poems you like and
what's called a language model on those
poems and then that language model will
basically learn these statistical
regularities in those poems and it will
learn to generate similar new poems
similar to the given ones so I asked
them after that Pippin right there
talked about how he applied machine
learning to astronomy because that's
what he's really interested in so he was
basically trying to help astronomers
find more supernovae in the space and
right now it's mostly a manual process
you know astronomers need to look at a
lot of images to find supernovae and he
saw that this is basically just a
classification problem so in this
problem you just need to classify each
image into either containing a supernova
or not containing a supernova and he
told us that he was able to apply this
relatively new technology called
convolutional neural networks to solve
this problem and this approach was
actually better than the state of art
method astronomers used to have them now
another example mark right there shared
with us was how they helped a bank with
their credit card customers they
basically built a model that would
predict whose payments would be delayed
based on the past customer information
and they also built a model to predict
if each customer would respond to a
phone call to remind them to pay yeah
that was an early client success for us
mark said that model and Bank in
particular was our flagship project and
so that was very successful
some of you okay and after that I asked
them what are the best resources for
learning machine learning and they
recommended a few one is FASTA AI it's
supposed to be a more of a hands-on
practical course in particular
I I recommend it to this audience
because it targets people who are
already coders and it's very well done
it was developed by a guy named Jeremy
Howard and also Rachel Thomas are both
very interesting and very good teachers
so please
and then mark here recommended the
machine learning course on Coursera by
joining from staff board it's also a
really popular course and apparently
it's more theoretical and more
mathematical than FAFSA AI another
bookmark recommended is this online
freely available book called neural
networks and deep learning by physicists
michael nielsen yeah I just really find
his writing especially clear ok so those
are some of the machine learning
learning resources they recommend it but
such an you right here took a slightly
different approach for him he got
started by learning statistics because
machine learning is partially based on
statistics and he didn't recommend any
particular resources for that before
stats I would personally recommend
getting started with Khan Academy and
Coursera and just go from there
you know I would say the same thing for
linear algebra too anyway after that Joe
added this this is kind of two paths
that people art argue for there's like a
top-down approach to learning in the
bottom-up approach to learning the
top-down approach is start with
practical things first start with how to
do it and then get into the nitty-gritty
of like what is the underlying
mathematics and the bottom-up approaches
start with them underlying mathematics
and then work your way to the practical
stuff if I had to guess I would say the
top-down approach will work better on
average for this audience but you have
to try for yourself and see see what
works and top-down approach being like
fast thought is faster i bottom-up
approach being like learning statistics
and reading the deep learning book but
you can good fellow or something like
that ok so one thing they all
recommended is working on a machine
learning project so you can learn from
the experience so I asked them how
should people find a good much
project to work on so that's Helens
advice and I asked them how should
people go about finding good datasets
for that and they recommended a few
resources for that like google's AI
datasets and after the event they also
sent me an article that lists good
datasets for machine learning so i'm
gonna put a link to that in the
description below too but after that
mark also said this for many projects
you'll find there isn't a nicely curated
data set if there's like something you
want to do or build and you'll have to
come up with creative ways of building
yourself whether it's scraping scraping
the web and then actually a few of them
also recommended this website called
cargo which is a machine learning
competition websites personally I found
that coming to be helpful for me to get
started an initiative that also offered
it's been a few hackathons data science
related hackathons around Toronto place
in the US and these places are good for
talking to other people find in the
field
talking to leaders who are the field
employees in the field other machine
learning engineers and good scientist in
the field
and I say eventually okay goal is
excellent but it only goes so far
I think on a huge skill that machine
learning years if you wanted to go into
machine learning in industry you have
this working with real life thing Cagle
is not quite really right if you're
interested and actually working I feel
like a lot of nonprofits actually
because they don't have in-house do
science teams and so a good way to go
about getting your hands on real data
actually
and it's just the other big French slow
motion engineers anyway after those
questions I let the audience ask
questions one of the questions we got
was why these are choose machine
learning out of everything and here was
Danny's answer and so I thought myself
you know what something does I'm good at
and that could be good at and then use
my skill as well and that's something
that I enjoyed and previewed like
machine learning because and data
science and when I got into it I found
that it was using a lot of what I really
really knew from physics you know it's
about explaining the world in novel ways
it's playing physical phenomena through
mathematical algorithms and the power of
your power machine learning that blew
away predict traditional predictive
classical analytics really drew me and
so that's what I really found
fascinating about and there's a lot of
promise in the future also them loved
another question we got was what
language should you get started with if
you're new to coding people recommend a
Python he said that it's because there's
a really strong open-source community
with Python and that you know there are
a lot of open source machine learning
tools written in Python and recently
there's also this really strong culture
of sharing what you've been working on
with machine learning and he said that
that's really helpful - and the last
question we got was what do you look for
when you hire machine learning engineers
one thing they mentioned is real world
experience they look for someone who's
actually put a machine learning model in
production ideally and Mark said that
they look for someone with real-world
experience as opposed to just personal
projects because there are a lot of
challenges that come up when you try to
apply machine learning to a real problem
in the industry and then Joe also added
this but personal projects still have a
lot of value I think I think people
actually underestimate the value of it
it's worth way more than a certificate
from a Coursera course or anything like
that worth more even than
the courses I would say if you do a hard
project something that really pushes you
and you write good clean code that you
would be happy to share and you put it
on github and you write a nice summary
of that that will make you stand out
more than the average engine alright so
that wraps up this Q&A session again in
the description you can find a link to
the entire raw footage as well as a
sign-up link for future meetups in
Toronto and possibly in other cities
I'm thinking maybe la San Francisco
Vancouver maybe New York maybe I'm just
dreaming here but we'll see by the way
this is not a paid promotion but if
you're looking for a job in machine
learning I would totally recommend
checking out this as career page because
it seems like a pretty cool place to
work out anyway thank you guys always
for watching my videos and I'll see you
guys in the next one
I've never even taken a single cs4 SAS
course University you don't need a
soldier