Data Science is one of the most popular
doing But why It's getting the public on
why all the companies are trying to use
the Adidas instance It's let me give you
the example because all the companies are trying
to use their data on getting the insides
from them so they want to use the
teeter sense technique So he that in our
mind we came up with the tutorial What
instead decides before said that Victoria didn't produce
that great learning us came up with the
brilliant idea that is free cladding Taqaddum where
if you get almost 80 plus free courses
on after complete your course containing certificate and
still on if you want to do this
poses by application that is also possible Using
grief Okay it started My name is Karthik
I work for a company called Latentview Analytics
My background has been on a lot on
the data management side of things they've ever
housing Business intelligence for many years I moved
locate of analytics ventilated you a year and
half back but as being on analytics for
probably 34 years now right Doing some hardcore
advanced analytics machine learning kind of stuff um
So what I would like to do today
right I'm sure you guys I would have
heard a lot of thing I heard there's
a logistic regression thing which happened in the
time series forecasting I'm sure you're getting a
lot of different imports in in multiple ways
Um and so in some sense that's really
the I would say the problem with data
science and machine learning and Advanced analytics is
that they're just lot of moving parts a
lot of different things that are happening So
what I'm gonna talk to you today is
more like a It's like a personal journey
off socks in terms of when you start
getting a lot of this information how did
you least segregated in somewhere so that you
can navigate through the space right And I
say a personal journey because I was terribly
confused You learn some stuff you learn specific
algorithms and things like that But how do
you kind of slot it in some way
so that you can you can basically say
OK I know this guy and you also
have a feeling terms off what you don't
know right in terms off you know what
You don't know that that's extremely important I
think as you can move through the space
because as they said it's fascinating in its
own way because there a lot of different
applications a lot of different ways you can
use it But unless you slotted in some
way and I'm talking about the just the
math part of the whole thing so well
the focus of this discussion is going to
be on the techniques right The quantitative techniques
will just touch upon all the other aspects
of business and things like that a little
bit But the core focus is going to
be on the math and the old guard
um part of it Don't worry if you
here dans which you don't understand because jargon
XYZ part of life and almost every discipline
in data science is no are different You
had a lot of dragons a lot of
different ill guarded them names and stuff like
that But the real essence the take away
that I want I want you guys to
take away from this session is essentially that
you need to develop a personal map right
in your own way as you go through
the score that even beyond this court or
saying these are all the different aspects off
data signs on machine learning advanced and take
whatever you want to call it on For
certain types of problems certain things are applicable
on what are some of the things that
probably you need to gain more information on
it That's essentially the bottom line on what
I'm going to discuss Today is my own
way of organizing the different areas within Within
this space right I'm going to divide it
I've divided into kind off 10 plus one
categories which I will discuss specifically on what
those categories are But of course that is
dark in there is there are things that
you probably might not have heard heard about
it earlier But don't worry about it That's
really not the not their sense of the
whole thing Okay let's get started So the
topic is navigating the data signs Boy just
a little bit preamble on why it is
so fascinating interesting and why it makes sense
for you to invest the time and energy
to do learn about different aspects of data
science which in some senses preaching to the
choir because you guys have already is part
of a course You have invested extra time
And I mean you had the motivation to
come and listen to somebody speak to in
some sense the razor already interested in this
area But the bottom Linus It is a
fascinating area because the amount of data is
increasing and that's an inevitable group And why
is it increasing it Basically because number of
transactions are exponentially growing a lot of interactions
are happening The social media reviews on e
commerce websites and all that A lot of
interactions on the other big chunk is observations
right Sensors kind off giving out a whole
lot of far data on almost every single
machine that is bearing replaces instrumented at the
sensor which collects a lot of information on
your seeing all this in our daily lives
in terms of how there is really data
sloshing all over the place within enterprises and
outside of enterprises and somebody has to make
sense of all this data for you to
drive business decisions Our society related down our
decisions are such governmental decisions and stuff So
it is accelerating a digital shift which we
all experiencing in our own way And ultimately
there is a data delusion There are different
types of data and I'm not talking the
structure data We're talking on structured data semi
structured data all different types of data that's
basically are out there But interestingly what's happening
is of course there is It's unlocking a
whole lot of newer use cases for this
is again from the vantage point of a
latent you right We see a lot of
different companies using data in many different ways
on automobile company trying to use sensor data
toe detect driving patterns I want to detect
driving patterns You can have better warranty claims
There are companies out there which which your
social data to identify trends in the marketplace
CPG companies fashion trends that there's so much
off information that is out there in a
social space There are companies trying to use
They've been using structure data like say movie
companies trying to the box office forecasting right
They're being doing it for quite abide by
now with extra data coming in from social
space they came back They can do better
forecasting each other My people use cases happening
across the space customer Intelligence marketing intelligence sales
intense and supple A chainsaw plating is a
classic pays off people instrument in the assembly
lines Getting all that data and trying to
figure cannot predict something that somebody like Can
I predict when a failure would happen Can
I predict whether this assembly line throughput with
radios so that I can take decisions accordingly
And that's for real I can assure you
that these are things you might have again
seen it in your own work life But
if you haven't take it from me that
these are these are problems solved by organizations
I'm their only scratching the surface at this
point in time On the interesting thing I'm
coming towards the fact that yes there are
always a need for using data to solve
business problems and all that But now there
is an increase in technology power which is
actually making it possible for you guys might
have heard of this whole neural networks thing
which started in nineties Came off just trailed
off because there's not enough computing power or
artificial intelligence if you take If you want
to look at some of this is fascinating
right So stochastic optimization which is the basis
for many of these machine learning algorithms is
a 1953 paper It's an eight page people
which everybody goes back and refers They want
to really look at optimization so it's not
His ideas are not new It is just
that now you have the ability implemented in
our practical context Why because of the cloud
because of big data because of the whole
sophistication of algorithms and detect power that is
coming in so new use cases the tech
cover all coming together helping to solve different
kinds of business problems And the digital shift
on the data deluge is a fundamental change
right So it is worth investing the time
to learn all this because it's a journey
every each one off You are trying to
invest time and moving in this direction I
think it's been bought it right That's the
idea of this life right So what does
it take to provide actionable analytics The way
we see it at a very high level
is about bringing these four elements together Every
single problem that you will try Installing analytics
essentially is a combination of four things at
the end of the day right The sophistication
might be higher Lower proportions my really But
it's all about bringing business data math and
technology together right Just I'll just repeat this
is the business part is about understanding What
is the business impact right off doing something
With data off off kind of running the
algorithm are tryingto implement a certain statistical technique
There's a data profit structured and structured sentence
structure data How do I understand it right
How do we make sense order for how
to explore it Official Isaac Then there's a
math part of it in terms of what
are the quantitative techniques that are applicable for
a certain problem Does it make sense to
apply that technique Because at the end of
the day there's no business In fact nobody
wants to develop a big complex neural network
right Why should you do that Or for
that matter implement a logistic regression if it's
not going to be in the business benefits
But the fact of the matter is there
is a lot of cases were applying Such
techniques is going to give you much better
Research will bring in the math part of
it And of course there's a technology angle
in the sense At the end of the
day all this has to get delivered through
our technology platform I'd be the Web interface
mobile application all right embedded in in classical
er type of applications right All these but
technology plays a big role in terms of
getting all this to the end user And
we also again of experience this in my
people based on your order from squeegee Right
there is an an electric thing that's going
behind the scenes but it all gets delivered
in a very nice seamlessly on your mobile
device You look at the recommendation system on
a flip Carter on Amazon that same thing
that's happening there is a very complex recommended
system that's running but at the end of
the date persons your very nicely of things
here about this you might be interested in
buying this also so technology is also fairly
critical At the end of the day if
you bring all these things together in the
proper way the appropriate manner you're going to
deliver good actionable insights for organizations So that's
the bottom line Uh I'll not go into
the details here but a quick look at
if you look at the business part if
it use case formulation becomes very important What
kind of use cases are there in terms
off for data driven decision making we're talking
about now Yes you've run and I'll guard
them You've got an output How do you
really interpreted in a business context Right It's
not The business is really not interested in
your you seem a trick or an r
squared metric at the end of the day
there saying with this metric what are you
helping me Lower cost increases Avenue increase customer
satisfaction How do you interpret the output off
your data science pipeline into what business decision
making pipeline Right That's what the whole interpret
ability aspect of it is all about And
of course domain expertise in specific cases are
data data acquisition How do you acquire the
data right there That exploration mutualization right And
data pre processing on the part of this
understand algorithms at an intuitive level What does
it do right Select the right techniques for
the right kind of problems on devaluating out
portable garden right by now he must have
realized running the algorithm is no big deal
right It's just one line of court and
fight on are in many cases it's a
Jew I interface also but really understanding what
the output of that particular algorithm is on
trying to compare right Using cross validation different
other other different techniques What what makes the
garden powerful so evaluating is extremely important The
tech part of it is about understanding I
d Ecosystem data engineering and architecture How UK
data is somewhere there algorithms here How do
you kind of bring these two together in
the most seamless way Because something will require
real time processing Something is battery entered Our
thing is fine right Some something that requires
so much of data that you need a
big data infrastructure I do need to scale
on the clothes So there are many different
aspects for building that data pipeline Right on
Of course after engineering is deals here I
see a lot of people from the I
t world So that way you will understand
the software engineering principle There's DLC how you're
on a project How do you test verify
validate or those are very important things he
went from even from an analytic standpoint So
with that so this is a mind map
that I have created right So it is
available free on the Web You can take
a located at your convenience but this will
give you a complete perspective off What are
all the different areas within People say analytics
right There's a business aspect to it There's
a complete data so I do cover data
management reporting except because I kind of lived
the world for a long time But the
focus off this stock on one that makes
the whole data science very interesting is the
quantitative techniques What kind of math related techniques
are going apply on data and why I
doctor started the most That's a secret sauce
in some sense and that's extremely interesting So
we'll focus on that So that is also
covered so far and where this kind of
a map helps used to fit it into
a context So if you know okay there
is this beep learning Somebody is talking about
this convolution neural network You might not know
though underlying techniques behind it but if you
can search for this on the map At
least you'll know where it kind of fits
in It fits into this space this area
and this is what is going to be
used for That is where you start And
then depending on whether you get that opportunity
a work life or your own interest we
can start delving into the details So I
leave that thought with you But this is
ah resource that's available on the Web It
it's just a mind map I didn't eat
looking and then figuring out You can also
search for specific terms and things like that
We'll come back to this I'll probably show
you this towards then Okay so what's the
real problem Okay it's great that there's a
digital share a lot of problems getting solved
and things like that You guys are learning
some of these techniques So what's the real
problem with the quantitative side of data science
The rial problem to me is this that
are just too many things right There are
just so many different techniques and every day
some new things keep coming up right And
it's not just structure data is unstructured data
semi structured Esso e I mean how do
you make sense of all this world Because
you still have to navigate through this at
the end of the day when yours in
here in the when you're in a position
very alert Applying an analytical technique right you
would probably be given a problem statement You'll
have probably tons of data now How do
you go from there Really say for this
problem These are some of the options that
you have in terms of applying that these
are some five techniques that are possible here
on Then how do you go from that
Really say this technique is what makes sense
And not only that once he applied a
technique you need to interpret output in a
business context So it's not a trivial job
If you really take that whole pipeline it's
not a trivial job really Go from just
a problems mate 100 statement and data all
the way up to a business in part
kind of a situation So how do you
navigate through the space is what the focus
is going to be on the way I
found out to be the easiest weight of
the water so just divided into categories right
So in my mind there are 10 plus
one I say 10 plus one for a
certain reason right Uh so So I said
Okay how do I how to make sense
of this And there is a certain flow
that I came off thought about it This
is again my own personal way of thinking
about it Some off it could resonate with
you It might be applicable from your context
also but I just explained how what the
floors at the end of every slide is
gonna have these two parts on the left
side of the navigation right What are all
the decision variables at the end of it
on at the end of that is basically
that category and I debate a little bit
on that category On the right side are
some of the details around it So that's
where the jargon will probably come in about
the different techniques and stuff like that Again
don't worry too much about it but it's
good to know Okay so on the navigation
if you understand one it's very similar and
other things also so I'll spend a little
bit more time on the first life and
I'll kind of flash through the other slaves
right Or go a little faster So the
first question right which is important Ask yourself
in your face with our data science problem
is is the focus on the process or
on the data This is a question that
many people skip on What I mean by
that is in a classical machine learning sense
What happened to your anger That data set
correct and said Here is the data Forget
about the data generation process It doesn't matter
to you In some sense here the data
kind of go ahead and do all the
things with data correct So the focus on
such an analysis is really on the data
But there are many different problems where the
focus is on the process which means what
is the process that generated that data Now
as part of this stock I will not
spend too much time on the process Part
of it I have one slide towards the
end The talks about process mining and stuff
like that But I feel it's very important
Ask this question because for example it though
if the whole problem is about understanding the
bottlenecks in the process Right Machine learning can
answer it for you If the question is
about what is a golden part in the
process our people deviating from the norm or
from the actual process just to get implemented
There is no way machine largest take regulation
Neural networks can't answer that question So if
the focus is on the process you need
a different kind of take me But we'll
start with the focus is on data Okay
The focus is on data which is what
bulk of the slide will be again There
are a whole range of techniques that are
there Then the second question that you ask
yourself is what is the type of data
right on the good thing there Is there
only three fundamental types of data right I'm
saying type of data from our from a
more structure rather than from all the ratio
ordinary data and travel date average raise might
have come down This is about is a
structure data Is it semi structured data Is
it unstructured Data structure data is very easy
for all of us to understand because many
of us have dealt with tables in databases
and things like that Rows columns clear data
types You know by looking at the column
You know what data type of this You
know the type of data Everything is wonderfully
organized You have matter data for those columns
So you kind off by looking at Did
you understand what the data actually means at
some level or disorder That so that structure
data which is what bulk of those I
T systems have have have really accumulated and
stole Now unstructured is also easy for us
to understand though we haven't dealt with it
in a probably an organization context Some off
you might have if you're working content management
systems and all that But most of you
might not And so instructor data Those are
fairly easy to understand It's about the majors
next where there's no structure to data You
really can't put it into a schema on
things like that and clearly defined what this
is Unless other than saying that it is
a moss off it's a wall of text
I thought It's a bunch of the majors
Audit is audio fight speech fight with does
not have structure to it at the start
at the stock Of course you need to
translate into some structure over a period of
time But when you're getting that data it's
all unstructured Semi structured is a little confusing
Can anybody tell me what semi structured data
is And not expediency come across Um since
structure even Excel sheet in some senses I
would say semi structured data because you still
have to infer the column by looking at
the column names right I mean there's no
understand what the data type is by looking
at those column names and figuring out what
the whole thing so semi structured data is
something like You have the ability to get
to that structure But it's not straightforward There's
some clue as to the structure of the
data but you still need to put some
kind of a thought to understand what the
data type is Can I interpret it as
a date or an impeach her Our float
right So it's not strictly defined right and
right becomes infrastructure Data is becoming a lot
more interesting is because of hold all the
I O T Stuff that's coming in any
sensor data that is coming to you is
in some sense Amy structure It will have
a decent structure to it But then you
still after the sun That structure by looking
at for example there was a project that
we did for automobile company Car said the
sensor data that's coming in from the car
site Eso Essentially the sensor captures everything about
how the car was driven Right The pedal
position acceleration engine temperature oil temperature ignition open
Was it on all kinds of stuff It's
all presented right It starts off The cities
are vectors That is there is a scaler
variable and all that But we're just looking
at the data You won't know what it
is You don't know what the first columnists
How is pedal position one of those 10
numbers right up front Nobody knows You need
to have certain level of expertise You're in
this case your doctor or domain export to
say Okay How does it really capture the
moment Somebody gives you that blue you say
Okay The 1st 10 numbers mean the panel
position The second five numbers mean the engine
temperature you can get to the structure but
it needs a certain level of processing right
on IOS devices everything emits on semi structured
data So you need to have a figure
your figure a way in terms off we
get all the way and I'm getting to
that structure OK so I spend quite a
bit of time and so one Okay next
Waters of one name off data I think
run amok database plays a big role right
up So for now I'm seeing a case
for the purpose of this life just for
our understanding We think it's not Web scale
data We're not talking about a Google or
a Facebook you're talking about Probably the gigabytes
Terabytes Rage right So I'm not talking really
Web scale data OK again I have a
category which talks about web skill So you're
focuses on data You're talking about structure data
In this particular case it does not scale
data Then you ask a question is a
time cities data I guess you guys have
done a time series forecasting Oh plastic on
why I am saying time cities not time
cities Because the kind of techniques that applies
again quite different greeting Ah data set If
it is that time cities as a non
time cities and trying to apply it will
just give you garbage Simples randomized Right You
can't randomize your data set because that is
ah there is a pattern There is a
time competent or a temporal competent with three
So assuming that it is not Time City
So the answer That question is no it's
not time series data Then the next question
you ask yourself it doesn't have a label
or a dependent Variable the moment to say
yes it has a dependent variable Then you're
coming to the first category which is probably
the most simplest and in fact a lot
of applications of scattered cases Simplest not in
not in the sense of the lettering it
There's still a lot of applications but this
is This is one area which is fairly
well understood right which is machine learning Supervise
the machine learning on structured data All If
you understand this the supervise machine learning on
on on on the structure data part of
it which is on the moment The next
question that you ask yourself This is the
dependent variable continuous or categorical it's continuous You're
talking regression If it is categorical You're talking
logistic regression classifications a logistic regression being our
implementation off it So I just quickly go
through this one's fairly simple thing But if
you keep this mind I think it makes
a lot of things easier when you get
to the other categories So the focus is
on data Then you ask yourself a question
What type of data you're saying It's structured
data Then they're coming noticing Okay Is it
Web skin Probably not Rep Skin at this
point in time is a time City stands
after that is knows it's not a time
cities data Then you ask yourself The question
is it doesn't have a label or a
dependent variable answers an s on Then you
ask yourself Is that dependent Variable categorical or
a continuous variable If it's continuous go the
part of regression techniques If it's categorical you
go through the part of the classification techniques
So this is category one for you Yeah
I think the one might appear straightforward but
still there is a lot of different wants
us towards How do you address this problem
again Some off some of this you might
have already come Come across an elect So
how do you do Exploratory data analysis That
is the area by itself Right How do
you look at the data How do you
organize your data on the whole thing about
data Pre processing outlier identification How do you
create missing data Because one of the biggest
thing with all these different techniques is it
is it is a situation where you put
garbage in You'll get some Fantastic You're probably
better off not implementing data science and machine
learning in some cases because your gut decision
probably might be much better But if you
feed in the right guide off the right
kind of data on the algorithms can actually
get the signals In that data you can
get some fantastic ISMs So data pre processing
talks about our players missing there are variable
transformations Then there's a whole aspect of features
selection dimensionality reduction Okay so essentially I'll give
you a quick thing Obviously people will talk
to you more in detail So the whole
idea behind ensembles is that you're not just
kidding So if you look at the largest
take regression right on what is called a
grammatical gotta logistic regression has a linear thing
to it Right on then You basically the
whole idea off logistic regression is to estimate
the parameters by looking at the observed data
You basically estimate your different coefficients all the
different weeks for the individual features And so
the mathematical equation is set You don't You're
not really You don't have the liberty of
looking at the whole hypothesis space but your
equation is kind of set up Easier example
would be a linear regression Why equals your
what are B zero B one x 22
extra and then all you're doing is part
of the whole thing is figuring out what
does b zero b one b two So
that's linear regression Largest aggregation is similar And
so you have some of these parametric algorithms
on somebody is about bringing my people things
together So you have a decision tree which
is an individual Al Qaeda You kind of
bailed my people please to estimate output right
And that is basically becomes a random forest
off course I'm talking in a very high
level There are no one sister it on
how you really combined the crease what you
look for and things like that But the
whole idea of on samples issue Take my
people beat learners your combined them together It
makes for a very strong learner bagging over
what Combining multiple algorithms boasting that is a
different technique But the senses is very similar
I take multiple things together each one predict
something on Then you kind of combine all
that together Give your final predictions Those are
the ensembles But I'm a quick non parametric
I just spoke about algorithms can be linear
or non linear type of algorithms cross validation
But it is extremely important on the salivating
outward then hyper parameter tuning You guys know
what hyper parameter tuning is So every algorithm
right has pedometers associated with it So it
would be the number off reason A decision
tree that's one parameter right How do you
know how many please to bay is a
10 threes 25 5300 priests right And similarly
if look at some of the random for
us they'll be quantified different knobs that you
can Actually I just like to get your
great performance So hyper parameter tuning is is
a way by which you can actually get
to the best parameters for that particular problem
So what people do is they do a
such and say Okay I will take the
different parameters put them in a great and
then try and solve the problem and see
whether I get better results on once it
gives The best result is the for parameter
that I want to go with So there
is a phrase by which you can actually
get to the actual parameter that the best
performance that's what is called a hyper parameter
tuning on Of course At the end of
the day you predict on the test set
you have a model that is built right
on your training data You cross validated You
have Look at either ensemble Stand alone All
that you've done on the end of the
day You have to predict this on Use
this to predict on test data So have
you guys done something in terms of training
a model predicting it on a test data
set and all that Okay so the lots
of interesting things that I mean it might
look a little simple but as simple as
let's say you there's are missing data in
a column Right on You apply the mean
So basically on the training data set to
say OK I'm basically for all the missing
data I'm gonna fill it with a mean
or the media There's must have done it
Now if you have to apply this model
on the on the test data you can't
again do a mean computation on the test
data Correct You'll have to take this mean
which is done on the training they don't
apply That I mean is a very simple
example But if you use one heart encoding
and more complex things on your training data
you need to do the same thing on
your test data You cannot do it Ah
fresh on the test data these are nuances
which are extremely important again The moment you
do it on the test data separately you're
not going to get the right answers Category
two is the focus is still on data
The type of data is structured It's not
not that skin not time cities It does
not have a dependent variable because no other
category is okay Unsupervised machine learning on structure
data right with its own set off specific
techniques like clustering is something that you would
have heard You're probably even done It k
means clustering hierarchical clustering but will be interesting
for you to know that are probably on
the 25 different ways you can plus the
data fairly sophisticated ways of doing blustering and
identifying patterns in your data But the bottom
line is you're you're creating so you're not
predicting anything You're just understanding the structure in
your data anomaly detection That's one of the
way we get a lot of things around
especially with respect to sensor data on all
that coming in detect anomalies Right So their
techniques by which you can actually take all
the data have a garden like isolation forest
and things like that It will help you
to detect anomalies in data recommended recommendation systems
I mean that's a 80 on its own
but again in our daily life have come
across many the commander systems on But there
are many different techniques right I collaborative filtering
Probably It makes sense for your quickly look
at some outside resources because it's a fascinating
area in terms of how do you know
recommendations How do you look at user views
of similarity How do you look at item
item similarity Right And then how do you
kind off correlate one over the other and
give a proper recommendations to people And it's
a great idea Breaks in We spoke about
the damage not introduction like PC a PC
a must have all heard Have you heard
of Peace Knee the's lease again A very
nice way off using dimensionality and you can
get it in a very visual context very
high dimensional data You can get it a
walk into a more interpret hable visual context
That's so This is unsupervised machine learning on
structure data so exploded analysis is still important
You need to understand the structure within your
data plus drink a means hierarchical many others
Anomaly detection is isolation Forest a lawyer for
about expansionists recommend those content based collaborative filtering
Highbridge that is under the think all self
organizing maps songs which is basically a little
more advanced way off doing clustering I don't
also identifying all players thanks So let's move
to the third category right by now You
must have guessed the whole logic Khoury focuses
on data type of data structured not Web
scale Yes it is a time cities at
the moment you come to time cities your
classical things might not work in order to
not work in the same way as used
to do for used used Rover A linear
regulation of logistic regression even for an ensemble
on typically time cities as a label associated
with it you're predicting something more often than
not just a continuous variable and white I'm
cities is extremely interesting Important from a business
Context is that's probably the first problem it
reports by any company can afford Cars demand
right Can I forecast sales because once a
forecast things better on forecasting is not new
Forecasting has been done by people for many
years So when we work with companies what
happens typically is they say the already are
having forecast Okay we've been doing You're 24
years of models has been running for a
long time Typically it's developed in SAS mostly
but they're not getting accurate forecasting and are
getting granular forecasts because the techniques have changed
drastically from what used to happen What are
15 2010 years back right So there's always
a requirement for companies to do better for
casting in my people different thing that really
look at it Forecasting is something that it's
very vital Right Sales forecasting eyes one part
of it You can do employee related forecasting
into infrastructure forecasting right So in the thing
that we do for Facebook it is basically
about the infrastructure for customs They need to
know harmony laptop They need how many servers
they need I store them to plan the
whole thing appropriately That's a classic forecasting and
it's a fairly complex problem Looking at them
are looking at supply your matching the two
So So there are specific techniques and good
that you guys have gone through a time
series forecasting thing But personally to me I
find Time series forecasting to be the most
difficult in some sense because it might appear
to be very straightforward By the end of
the day you never get good forecast For
some reason it's probably just me but up
so on There are specific fundamental so to
say off understanding a time cities before you
can start modeling on top of it So
in your course again you must have heard
about stationary and non stationary time cities You
guys have gone through that so back Quite
a way for me to understand How do
you station rise of time Cities Why is
it important and things like that where the
techniques by which you could do that right
How do you decompose time Cities Look at
your training Look Atyour levels Look at the
noise What's called white noise and things like
that And how do you do Auto correlation
plots is here PCF How do you detect
their difference ing All that are very interesting
techniques which would which will not find in
a typical supervise machine learning problem Off course
feature Engineering is also it's very interesting in
a Time city scenario on how much off
moving averages and lags that you want to
calculate right in order to get better focused
I building time series forecasting models The classical
way of doing it is a dream our
whole winters and things like that But read
more recently that a lot of deep learning
techniques that are coming in right So for
example something will recommend neural networks All right
now gives you probably the best the cells
when it comes to Time series forecasting It's
a deep learning model It has It has
It is used for sequence prediction Problems typically
and time cities is nothing but a sequence
prediction problem I have a sequence trying to
predict the next sequence my before 12 months
or the most immediate data point whatever that
might be But you're still pretty in the
sequence Yeah so that's basically so what I
also have Finally I'll probably show it towards
and us for each of these categories I
have some sample illustrative court of course certain
and fighting because that's what I'm comfortable with
It's part of my get Habre Possibly right
Not probably the best possible court but it'll
give you an idea If you want to
get started in an area how do you
really get stuck in That could be some
template for it I'll show you towards A
Okay so on that Corbett I'm going which
is there in my get help basically uses
Adhemar based Time cities for custom Um Category
four This focuses on data but now the
data is unstructured on I'm kind of marketing
semi structured structured under the structure things so
I don't have a separate category for it
because there's one extra stick to make it
structured But the really big differences unstructured data
the momentous Ian structure day They are talking
text and uh things like that Volume of
data probably not Web scale at this point
in time has labeled dependent variables Now if
the answer is yes then you're talking about
supervised machine learning on unstructured data Most simplest
classical use cases span protection How do you
get X family made a state that is
text So you need to You need to
understand the text structure the next right But
the end of the day are still predicting
whether the email is of this prime or
a ham The easiest example But something like
sentiment analysis image classifications all that falls into
the hole supervised machine learning on the instructor
data space I Provence structure data It gets
very interesting here because depending on the type
of unstructured data there are different techniques Again
different type are at every of techniques over
this text It's all about natural language processing
Having done anything on text analytics natural language
processing you've done You do it as part
of your daily job Is it as part
of your work OK I mean you would
appreciate the amount off one It's extremely interesting
and I know we're just scratching the surface
of the whole thing Correct We probably use
a bag of what boards model You take
the terms back prize it basically use it
for model But there are very sophisticated techniques
that are out there Honey let's talk about
that a little bit again It might appears
jargon if you haven't done external itics NLP
but it's good to know you're talking about
images now You're talking a bunch of images
I feel if you look at cattle and
things like that a lot of competitions now
is about image classification I think there's a
recent one where that was actually a sound
thing Using the sound that is emitted Can
you detect a real or not operating one
of the recent things But there've been many
Kenya from the pictures Can you detect cats
and dogs Is a classic learning problem in
cabin but images is becoming very very big
notice even in a business contact because nobody's
even touched those images But now people are
starting to think if I can actually get
Ah interesting insight from images Now the world
is anyway I mean we keep clicking so
many photographs so many things And there are
so many pictures on your mobile phone Just
imagine an organization that is just so many
majors that are there one problem that we're
working on just okay to motivate you in
this direction Ist So we work up Pepsi
on the assembly line The sensor basically takes
a lot off photographs right I mean what
they really want to do is using the
photograph can predict if x bunch of photographs
and they actually have labels associate It says
defects not defects on Then once you train
your CNN or your convolution neural network for
the new images coming can you detect any
major knock can detect a defect or not
Right now you can expand it in multiple
different ways in your own your own customer
situation in your own Whatever you're working on
I think it's good toe Figure out whether
they're specs that is in water Can I
do things better or can I Can I
get some inside sort of text images if
you're dealing in some riveting majors and stuff
like that Probably There is Ah there's a
belt of information out there on if it's
so in other types of unstructured data We
spoke about extending majors in a kind of
a standalone manner but speech is next having
a temporal nature right Or or there's up
There is a sequence associated with it right
Audio Each videos for example Videos is nothing
but in majors In a sequence on for
that there are different techniques and the state
of the art of something called record and
neural networks are ordinance It is useful for
sequence prediction So this category is all about
this category is all about You're still doing
supervised machine learning so all those other techniques
still apply But you need to figure out
a way to take all this unstructured data
couldn't work it in some way to a
structural form and then start applying the different
techniques on top of her All right so
NLP is probably the most interesting because there
is text People of already started doing quite
a bit with text and now you can
do a lot of things with it fairly
sophisticated things with text so that is that
is where the maximum activity is be done
using text for our predictions and classifications convolution
neural net rocks and ordinances used in a
big being specific industries as of now But
this is also want to become mainstream because
majors and although there's not being touched at
all the organizations still know So there's a
lot of opportunities over there Andi have you
guys done neural networks artificial neural networks or
you get started doing it Okay well we
just got so in and is the basis
for many of these different things But there
are very sophisticated architectures right that make a
scene in different from a classic Arctic CNN
and also from recommended neural networks have a
memory associated with it makes it different So
that is some Okay there's a beef Dicle
Let me let me go through it So
why is text so interesting for people is
basically because of this right one There's a
lot of unstructured text What they say is
80% of the organizations on structured data people
haven't really tapped into it so every organization
wants to do something with it If somebody
use some off use from the content management
side You would have seen when organizations implement
CMAs They threw in a whole lot of
documents into it But all the best they
will do is probably searched through it Keywords
such There is really nothing that they get
out Get out of CMAs other than probably
such which is which is important zone with
But now with this kind of techniques available
you can actually extract a lot more insights
from text on Next Typically packs a lot
of information within the shock window right So
if you look at this particular piece of
text it captures issues product urgency organization customer
That's typically a lot of information available within
next Andare Text Analytics is a foundation for
any higher levels of talk about artificial intelligence
and all that Unless you can process text
speech audio and things like that No really
con are barely intelligent agents because that's a
very important part of how we live Our
daily lives right Next is important Windows in
tow Words make sends out a forget the
context from these words or sentences and things
like that again this this light I rushed
through but just for especially for a person
who has already been in NLP to know
there are a whole lot of techniques on
fairly sophisticated ones extremely interesting in terms of
the power off these techniques So at the
end of the day what do you want
to do with text Corpus is basically four
things One You want to convert text to
some numerical output so that we can use
standard machine learning algorithms which is where counteract
risers and you can you convict rise text
and given number two that would write what
is called a bag of words mortar on
Then you can use it for any kind
of a mission Learning prediction So that's one
thing that you typically do You can extract
content from individual documents in the carpet so
you give a car person say Tell me
whether the names of organizations which is there
in this Scarpa's wreck our names of people
write all this parts of speech tagging entity
extraction But all that is also you can
do with you I do with Tex then
dimensionality reduction You want to reduce the dimensionality
because if everybody becomes a dimension they're just
millions of dimensions which is very unpractical on
then extract relationship between words and documents in
the corpus So you have again a whole
lot of techniques off It's the most famous
one of people have heard from your heart
if it has worked to break So what
the heck is how Google runs its search
right now about the practice convert word toe
individual vectors This is a topic on its
own But good to know that there are
I mean I will be good to know
that these are the different techniques state of
the art in terms of handling texts right
And this is a potion by itself But
if anybody is interested this is a great
idea to be in because I think I
would say the next five years there's gonna
be a lot of expertise analysis and my
people forms and shapes They're already see either
seeing organizations start delving into it a lot
more Uh but suffice for you to understand
given a body of text that are raised
by which you can make it structure their
ways by which you can extract meaning order
for like you can extract inside So the
first you can understand the semantic equal in
right That means what is this world equal
and do so The most classic example people
say is given a corpus of texts if
use vote back right which is basically what
is called a board algebra is that then
you can actually have algebraic statements and King
minus man plus women right Women gives your
queen You can actually start doing those kinds
of exercise what is called a Florida keyboard
Algebra with extra accents And why is that
possible Because everything is codified as a vector
There is a way by which you can
convert each piece of wood into a vector
on the momentous affected You can do all
sorts of mathematical manipulations on top of it
it's not anything else Just go and look
at word algae brought Ryan Look at some
videos You will find it extremely fascinating on
how you can actually do this with words
All right Okay So let me just quickly
more I think I have another green OK
Category five So we have another five top
five categories to cover Um so focus is
still on data Is unstructured data not web
scale no label by which means you're unsupervised
machine learning on unstructured data which is again
You use the same type of techniques in
some sense But here you know I don't
have any different variable dependent variable You're trying
to understand the structure within next or you're
trying to understand the structure within the images
or you're trying to understand the structure within
your It's one of our audio and things
like that I put a question Mark said
because I don't know whether CNN's and audiences
still applicable in this area I haven't done
much investigation into that space but I know
for a fact that a lot of NLP
that is used for just unsupervised machine so
some of the topic modeling and things like
that actually falls into that category right You
have a bunch of text Can you divide
into specific topics A whole range of NLP
can utilize what you saw in the previous
life right part of speech dragging topic mortars
aboard and ratings and things like that There
is something called Ganz which is this is
all generated networks that also neural networks that
generate new things Ah neural networks right movie
scripts In fact that movie script There's a
famous example of a movie script written by
a neural network that one some fifth prize
or six plays in our particular film festival
There are things that create new art There
are no networks that create new music So
this is one area where your networks are
becoming generated by training the newly crowned with
a bunch of let's say our Picasso paintings
it can actually generate new types of paintings
that can be that is very similar to
the previous one that I don't see there
are fairly advanced I haven't seen this in
a more business context but these are some
interesting developments that are happening which will come
mainstream probably Or a Peter Yeah DNS is
generated models right So it's generated adversarial networks
is so examples or something So the moment
of your generating stop which means generating you
are generating new music on the most classic
examples generating movie scripts So what these guys
did what they fed him a bunch of
Hollywood scripts to a neural network It look
at all the patterns and things like that
It actually the output of the nearly broke
WAAS a new piece of movie script on
which they actually got in actors to act
on that script and that once in my
ward and stuff it's a movie called Sunshine
And so the Munich where they're coming in
which are not only just designing patterns to
kind of critics whatever test data eyes but
it is actually generating new types off on
That's the whole on its called adverse cereal
because they're to the neural networks that worked
together to make these things happen At 11
little network generates it The other new neural
network verifies that whatever is generator this is
good enough And so you have to neural
networks working in conjunction I'm quickly moving on
So Category six Category six is essentially it's
order scholars reinforcement learning if look at machine
learning Machine learning is divided into three categories
Right So provides machine learning unsupervised machine learning
which is what we saw earlier on that
is this whole area called reinforcement learning right
So what happens here This It is structured
on structured data Typically the enforcement learning of
in wall what But it can also work
in work with structure on structure data but
really the difference here is the feedback from
the environment right on the simplest way to
understand this something like a give right And
so Lord of working Reinforcement learning happens on
game environments Right So when you play again
right a Pac man or even complicated inflict
doom and stuff like that you take an
action But there is also action coming in
from in this gun in this case a
particular game But then think of it like
an environment right So environment gives you certain
kind of response on At the end of
the day you get some delayed feedback ready
to win the game lose the game you
score school so many points All that is
possible So how do you really do machine
learning or how do you make an agent
learn In that context where agent takes an
action there is feedback coming from the environment
right on This feedback is not the ultimate
objective The ultimate objectives Winning the game It
will happen probably after three Levenstein level stand
levels Whatever that might be How do you
take that feedback and take the next best
action So if you really correlated that's what
artificial intelligence is right if you want tohave
Ah about right Move this more around this
place It basically has to figure out Should
I take the right Are the left right
now I'm hitting an obstacle If I'm eating
an obstacle how do I take back and
hold away Move forward So this is all
something You have taken action relate to the
feedback and take a next best action on
At the end of the day you probably
achieve your goal of probably Visar What It
has to probably cross this on the beach
the other day but it can be fairly
sophisticated What many other different types of situations
So that's the whole area called reinforcement Learning
more than a business context I think this
is their artificial intelligence Kind of comes in
right How do you have How do you
develop intelligent agents that can take actions Not
with So when I said delayed feed But
just to clarify that a little bit If
you look at supervised machine learning you're getting
immediate feedback Right now you have a label
So your new prediction you have a label
which tells you this protectionist so far away
from the actual one you get immediate feedback
on your single raw data in the case
off reinforcement learning you're not getting that immediate
feedback You're getting related But take any game
chess for example You play a more open
please a moment the end of the day
The feedback is about winning or losing the
game which comes much much later How does
how does an agent evaluate the position at
every single point in time and take the
next best action is what the whole reinforcement
learning area is all about All the good
news is there are algorithms that are fair
I mean again if Europe Eitan guy are
you have you can implement it on your
laptop once you go back to your rooms
are home right Probably take three Ask implement
our reinforcement learning algorithm So that does not
That's not the limitation anymore right that this
court available in fact in my guitar by
reinforcement Probably having putting up is a court
But it's readily available I had that but
on there are all these autonomous cars that
is a lot of talk That's all about
reinforcement learning and artificial intelligence right Think offered
That's the other end of the spectrum a
car has to kind of drive itself under
its getting feedback from the environment In many
different ways There has been bringing right obstacles
pedestrians signals Just imagine the complex Theo that
on was Actually it's like it's happening It's
just a matter of time before kind of
spreads all over the world again I've just
given sometimes in time So I lived up
presentation with you So please some of these
storms you can take a look at it
and see what are some of those techniques
that are out there on on These are
all not static These areas keep moving the
rapidly evolving areas So a month down the
line there'll be some new algorithms and new
implementations that have coming Um okay And Fargo
black forms Okay That's basically what the reinforcement
learning is I'm not going to spend too
much time but I just want to introduce
the stop it called daisy and machine learning
Because this is again not something which is
many I would say talk about But if
you look at machine learning the biggest problem
with the machine learning output in a business
context is you give up prediction right You
give what is called us A most likely
would estimate you say Here is a bunch
of training data and I'm predicting on a
single variable for you or a single prediction
for you Now in certain contexts that is
okay But in certain other context that's not
acceptable because you don't know the uncertainty around
those predictions So just to give you an
example let's say this is about our detecting
something in a patient For very fair weather
you have to have a surgery on the
patient or not Right said That is what
your machine learning world are used for right
Just imagine from a doctor standpoint you're giving
a largest take regression kind of a prediction
and saying there is a probability off 0.8
great wave sold Whether the particular diseases there
are not right you really can't take a
did The doctor cannot take a decision just
with a point estimate What you need is
a level of uncertainty in estimates Level of
uncertainty comes from the fact that you give
a prediction and say yes this is the
most likely hood prediction but all the other
types of things are also possible you know
in a range that is what Beijing mission
only helps you to the basin machine Learning
is all about giving not only the predictions
It also gives you the uncertainty around the
predictions And it is not a new area
in the sense that you have done linear
regression that is basic and linear regression You
have been logistic regression that is Basie and
logistic regression So it's not a completely new
area by itself But the thought process is
different I just leave that with you guys
because from a business standpoint uncertainty is extremely
important for a business guy to take a
decision and tell the stakeholder You know what
I'm taking this decision But this is how
uncertain notice rather than giving one particular estimate
So personally I feel there's a great area
to explore because at the end of the
day if you really have I really want
your business to take a decision you need
to provide them a level of uncertainty our
estimate Otherwise they're not going to trust you
So again there are multiple packages multiple ways
of doing this again If you have the
time please do check on some simple thing
around OK You know what a linear regression
is See how Beijing linear regression is different
on what are what does it provide so
that you have comfortable about the fact that
okay now this this is something over and
about a classic our machine learning kind of
situation so that I poured Uscategui seventh But
I do have a notebook in my guesthouse
which very completely take a look at it
in terms off what that is all about
uh statically eat this Okay now we have
done supervised unsupervised machine learning on reinforcement We
have looked at it Look at texting majors
all that But what of the data's that
skill You can do certain things on your
laptop or with probably with enough memory and
of course on your machine But what if
you're talking really web scale data terabytes of
information How do you skill machine learning So
scalable machine learning is an area of its
sense Right where you are talking about things
like spark right Big data cloud All those
things come together When you're talking about scalable
machine learning the movement you say Yes I
can do things on one machine for a
certain volume of data But as it's killed
someone you're looking at Web scale data The
same thing will not hold her How do
you scale How do you use a spark
in China for So uh so bottom lightness
again that is This recently came across this
thing called Hitch to right That's also a
big ground up for scalable machine learning right
This is an area of it'll again expand
rapidly because every organization now wants toe most
of the work that we do again Cloud
is a de facto thing We don't know
anything on Prum ice right now because just
a volume of data is just so huge
And just us the car since that day
I was talking about you're talking about every
single car in 158 countries on the wall
Um of data is just so huge You
can't really put it on anything So you
put it on You start black phone call
data bricks which is essentially spot on the
globe right and used tons of machines to
process all the data All right The good
part is it is not It's not costly
We was going to use it for a
certain period of time Yes you're going to
use that power for a short period But
it's May I come back to the benefits
that you can get out of fit The
cost is not prohibitive it on So big
data Cloud is gonna play a big role
because now it's all about scalable machine learning
How do you skill across computers on the
good part is in order to learn how
this works you don't need a cluster of
computers You can still do it on your
laptop so you can write a piece of
spark court spot again Linear regression logistic regression
position please Whatever that might be The man
it on your machine Okay see what Howard
works except the same court essentially works for
thousands of machines in a cluster Don't have
to even change a single line off court
because there's something on a spark session Contact
with ups tracks toe the distributed nature off
spark We're gonna have to know anything other
than knowing how to work with spark in
a machine learning context Okay there is also
this fascinating area called are such If you
come across elastic So it's so large and
things like that There's a lot of activity
that is happening in terms Off You throw
in a bunch of daytime toe a search
platform right and it indexes everything on top
of the second round machine learning you really
don't need I mean least people like me
used to spend a lot of time creating
data warehouses by 40 years Data march On
top of it you're on certain types of
reports and stuff like that Some of these
search techniques is very simple You just have
the index it into elasticsearch On top of
that you can run reports You can even
run machine learning on it straight of it
You don't need to really invest time and
developing scheme are I do all that kind
of stuff Not that it is a simple
is what I say but potentially you don't
have to spend really diamond in building all
those different black phone second each layer of
the black matched to wait and see That's
the complexity and things like that right So
there's again a fascinating area and down the
rental price Such okay so that's scalable machine
learning That's Category eight I'm not going to
talk about the slight I leave it with
you on How do you make sense of
a big data again If you look at
any big data If you look at the
Big Data 2017 picture the landscape it has
a 200 Our technologies black forms techniques totally
over But the way to make sense is
to divide again And so some areas saying
okay distributed started to secure that processing sequel
access machine learning right some categories and learned
certain techniques around it So that again what
a period of time if it's really required
you can get into the BT the reach
of those technologies But big data is there
it is there fore especially from the analytical
context It is going to be big It
is already kind off getting and almost every
single aspect off machine No Okay so that's
big data Category nine ISS OK Category nine
is optimization It's one thing tohave predictions You
can give up predictions but if you put
it in a business context the business has
constraints wanting to say that the demand for
your product is going to be whatever So
many units 100,000 units right But the business
will have constraints in terms off Okay how
much can I really produce At what cost
So there is a lot of constraints on
how much how much of this prediction can
actually fulfill So at the end of the
day every analytics pro I wouldn't say in
every many analytics problems have to end with
an optimization thing because if you're putting it
in a business context of business is going
to say predictions are fine but that these
are the kind of constraints that I have
The most classic case off optimization that we
do is like what is called a media
mix morning If somebody has done that you
know the marketing manager basically has this problem
off How much money surely are located Different
channels so you can have some aggression on
all predicting thing This is the kind of
money that you have to invest to get
the maximum the money you can invest but
that are going to be constraints that they
cannot take all the money out of Let's
say a TV and they still have to
spend something on TV Edge because they can't
really read the more they from that medium
completely so that I want constraints in that
problem So finally left a solvent optimization problem
their minimum maximum We heard of optimization So
I'm sure you understand the language that I'm
speaking at the end of the day You
have to put in those decision variables the
constraints and solve the maximize or minimize as
the case may be on The good thing
here is if it combined machine Learning with
optimization makes for a very strong off work
because predictions are the are put off your
good machine learning algorithms that have already developed
evaluated on your also kind of putting it
a door business context right And so I
need may not be having a business context
again I have a piece of court which
basically talks about using optimization in your daily
life Eso the example that I have which
I submitted a blogger on is in terms
of okay If you have to really optimize
on harmony Ted videos you've got to watch
You have certain amount of time that the
Fed videos are all available which runs for
a certain period every time video runs for
a certain number of minutes or whatever right
If you want to say Harmony Ted video
should I watch for the next six months
One year I have so much time on
I don't want to be overloaded with stuff
Can I use a linear optimization to solve
the problem If you want to go on
vacation you have a certain number of days
available All right you have a certain object
that you are You have You have What
vacation should you take You can use it
for all kinds of problems And of course
it's very popular in a business setting Also
over 11 In a business context many optimization
methods are available on optimization is again a
very well researched topic But the good thing
is it is all available again and are
fighting and things like that So Fighting user
Package called which is basically a package to
linear programming I'm sure are also scored a
package but essentially began going back to my
old your thing If you know what you
don't know that are gonna be opportunities for
you to do all those things on your
left But the key thing is to know
what you don't know Figure out Okay This
is one area which is interesting What do
IQ as long as you know that but
back exists and that is used for this
kind of purpose You don't need to know
What about direct rate You can learn it
as a man An opportunity comes But if
you don't know that tomorrow and somebody asking
a question you can't even figure out what
wants of their personal How do you have
an intelligent conversation That is why the breath
and understanding is important The death going to
ask for your requirement on the need because
going to protect across all these areas is
just not humanly possible The last one the
last one Ultimate oneness machine learning in production
Right Which is about yes you can do
all this fantastic stuff How do you really
get it to the end users the Lord
of questions that you have the answer to
really push machine learning production So you need
to understand the technology that technology probably you
need to understand some kind of a more
so you need to understand the ecosystem on
set a bunch of questions I'm saying Okay
I'll see quickly Are you going on the
model How is how are you going to
get the feedback right to feed it into
the model for your re calibrated many aspects
toe running machine learning in production on at
the end of the day all of us
want to do that It's not We're not
just learning machine learning for fun Our data
science for fun We really want to implemented
in a business context on the business or
anybody has kind of taken action on it
So my people things in terms of getting
machine learning into production the last one Nice
attend plus one Because this is where we're
starting to think if the focus is on
process not on the data Right So all
along we have only seen cases off your
given data or your extracted data on your
working with data on multiple different ways What
of the whole thing is about the process
and typically the process date all structure and
what I mean they process in the typical
sense is order right known application process Everything
has a series of steps associated with it
on the question ISS basically Okay What are
the bottlenecks in my process Where are the
deviations in my process Can I discover the
process What is called a process discovery Using
using all this data on there is a
vehicle process mining again Nobody talks about it
in a very big way But if you
are a person was in person more on
the process Especially if you are on the
sub plating and areas like that Right Process
Mining is probably more important than data mining
or your machine learning because ah but machine
learning please As far as I know you
got answers Questions like this you need on
this is also a fairly well researched mathematically
strong area You have a bunch of algorithms
which will tell you how to identify bottlenecks
How do you identify performance metrics Right All
types of very interesting stuff If you are
a process persona just complete this If you're
a process person check out process mining dot
org that solution Wait so you can just
make your carrier just looking at process mining
other visibly But I just want talk about
three key skills that they need to acquire
to be successful in analytic space again This
is just my personal views One business connect
every problem Unless you're able to define it
in a business context Analytics is in materia
That's one key learning that I have So
if you are especially a hard core technology
percent which in some sense I waas earlier
the biggest roadblock are the biggest thing that
you have to cross this about looking everything
from a business standpoint and asking the question
of So what So what if we implement
this algorithm So what if you get this
kind of metric space at the end of
the day How is the business going to
get impacted is extremely critical in this space
probably the whole narrative has to change in
terms of people talking about this was a
problem that is being tried to solve It
is the price And so this data driven
decision making is important So I would want
to bring that up front again some categories
and things like that But the key thing
is that might be programmers out here who
are very comfortable doing are fighting and things
like that So no problem You can pick
up your are toe programming language of choice
and go with it But there could also
be non programmers Right on the I've seen
at least people coming Toby and saying I
feel left out in this whole take Great
people talk about a lot of algorithms but
I'm not able to do it because I
don't want really sick and learn fighting for
no I'm I'm are from a domain export
Having invested so much time doing this The
good thing is that I would say Please
look at some of these doing based tools
right If you look at Microsoft as your
machine learning plays of look at it it
is basically a way to assemble things together
It's all Do you eat this You don't
even have to write a line off court
Right hit stool is another really classic platform
Big Big ML right These are all platforms
which are completely do I driven Even if
you're a programming export I think would be
good to know some of this back from
the iconic because you can do things much
faster especially the program sort of proto type
things and quickly get some predictions and stuff
like that It's amazingly easy to do it
on as your machine learning platform and as
your machine learning its fleet by the before
I think upto around 10 gv off data
I think you can pretty much do all
your practice even for fairly large data sets
using as your machine learning So coyote as
your machine learning buyout hedge toward are the
i R A fairly sophisticated algorithms Do I
based this on the cold floor But it
has then of course Big Yemen is another
platform It is very interesting So and I
think there are quite a few others also
name and things like that So the slightest
basically said non programmers may not feel left
out You can develop an infusion for all
these different algorithms by using a Dubai based
on models Also I think technology landscape is
another thing Don't don't get school focused on
just the data science machine learning part because
at the end of the day this has
to get implemented within an environment So think
technology landscape again Expertise is not the key
thing here If you already have expertise great
But at least a feeling terms off Okay
how is it going to get implemented What
questions should you ask or you won't need
to answer Cloud mobility prep technologies Embedded analytics
Legacy systems Just have get a flea for
it Flour probably is probably the most important
If you ask me right now in other
three years I don't think that any analytics
without the club everything will be on the
club because that starts the power of the
cloud especially when it comes to scalable machine
in proto type stuff But in a real
production setting it's going to be there on
the cloud most of the time because that's
eliminator that you're dealing with So that's basically
what I had All right thank you very
much for your patient hearing invested union being
a long what is D decides on what
are the topics and what are the concepts
Come sentence you like a pendant That means
to you doesn't the common section I'm also
if you want more more about the sense
it then please goto great landing had any
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