April 1, 2020
Learn Machine Learning | What Is Machine Learning? | Introduction To Machine Learning | Intellipaat

Learn Machine Learning | What Is Machine Learning? | Introduction To Machine Learning | Intellipaat

hey everyone welcome to the session by
Intellipaat I hope you guys had a beautiful day and in this session we’ll
quickly have an introduction to machine learning and learn a lot about machine
learning guys so before we begin with this session make sure you subscribe to
Intellipaat’s YouTube channel and hit the bell icon so that you never miss
an update with us guys so on that note here’s the agenda for today number one
on the agenda is we’ll quickly have a have an introduction to machine learning
and then we can check out how machine learning has been spread around us after
this we can check out the types of machine learning quickly and then we can
walk through the process of machine learning guys and after this I’ll show
you a quick demo code that we have put together for this particular video and
then after that I’ll be guiding your way to go through the fast-track procedure
to becoming a machine learning engineer so on that note let’s quickly begin with
the first note on the agenda guys introduction to machine learning so the
first thing which would occur into your mind the question is that what machine
learning actually is right so the machine learning you know the definition
has many forms and figures but the most simplest one which you can grasp and
understand easily is this machine learning is an application of artificial
intelligence which provides the system’s the ability to automatically learn
automatically improve from its experience alone without being
programmed by a human guys so what we’re basically trying to tell is that we’re
gonna teach a machine so that it can learn from its own previous experiences
or rather than having been explicitly corrected and taught by humans guys so
this is the most simple ten definition of machine learning that can exist the
key word that you need to check out here is that without is a very strong key
word because you know it learn and improve on its own without being
programmed by human beings guys so I hope the definition was clear I’ll leave
it in the screen so that you can pretty much you know take a look at it and note
down if you guys so on that note what is machine learning
now well guess to give you the most simple
you know to put it in the most simple way machine learning is basically
algorithms on steroids it has mathematics in full-swing
steroids along with computer science good guys that is the most simplest way
I can tell you what machine learning is but then when you talk about the future
there’s a huge gap between now and what we can achieve in the future because in
the future machine learnings primary goal in fact machine learnings primary
goal way back in the 1970s is still the same as it is today it is to basically
achieve artificial intelligence so by artificial intelligence what do we mean
basically we need to get our machines to be almost as good as humans if not
better well if they’re better than they might pose a threat to us but then there
are so many things that machines can do which humans can not in that case pretty
much this is the future this is the goal of machine learning guys achieving
artificial intelligence to its fullest you know at the earliest in my opinion
so this brings us to this chart where we can pretty much see how machine learning
is a part of artificial intelligence well artificial intelligence is the
umbrella under which machine learning under which we have deep learning guys
so it’s this complex concave structure basically what we’re trying to tell you
is that deep learning is a part of machine learning machine learning is a
part of artificial intelligence guys so if you guys need any more you know
in-depth tutorials or any other learning material and resources make sure to head
to our YouTube channel after this video and we do have blogs well in in depth
about deep learning in depth about machine learning and in depth about
artificial intelligence and loads of other things as well guys so make sure
you stick to the end of the video again as a surprise to all of my viewers again
at the end of this video I do have something planned out for you guys and
pretty much you can check out all the resources later as well and I’ll be
guiding you through the same again guys so this brings us to the question of
where is machine learning used around us oh well it is so subtly integrated into
our daily life that we do not even notice it anymore right so when you
think about it let’s say you you and your friends you go on
Vacation and you click like hundred pictures and pretty much you’re back
home now and you wanna put it all on Facebook as soon as you put it on
Facebook you do not want to tag you know each person let’s say your friend people
are in hundred photos so you do not want to tag each photo each person right but
then due to machine learning or Facebook recognizes in fact where you have gone
with whom you have gone at recognizes the face so you can pretty much you know
Facebook will tell you if it wants you to tag that person or not so that is a
method recognition and it’s working and it is so subtly imput implemented guys
and then coming to malware and spam detection I am sure every one of us here
has a mail account right so whenever you’re checking your emails or even SMS
is these days for that matter so pretty much Google will alert us if
it’s Gmail or any other mail provider for that matter as well pretty much mail
is filtered into your regular inbox Priority Inbox and spam so how does you
know this particular you know mail provider know what the spam and what
does not again machine learning is in play here then coming to predictions of
the future well prediction hasn’t not you know
probably you know predicting the outcome of a cricket match or a football match
or something I mean sure that’ll work as well and these days we’re already seeing
sports predictions with machine learning but what I mean by predictions in this
particular context is pretty much analyzing trends and analyzing what
pretty much can go about it so you have some data right now and you want to see
what that date with all these data what will happen in the next ten years then
sure you can go about doing that as well guys you know sports predictions are are
a big thing today and if you guys you know are not into a lot of for sports
and pretty much you should check out because it is extremely interesting as
well guys so on that note the next three machine learning things around us are
pretty much sure in voice recognition as well because when you come to think
about it there are so many things that are there is voice unlock on phones
right now you know there is voice recognition for televisions for
Playstations and so much more so having knowing that pretty much you
know you are talking to your particular phone or your gadget and it recognizes
that it’s you who’s talking is another big thing guys and then coming to social
media just give you the example of Facebook there are so many things there
is machine learning walking right
now that you guys are watching this on YouTube so you just watch Intellipaat’s
machine learning video and then the next video series you pretty much will be
recommended to is our Intellipaat machine learning courses and much more
as well so how does YouTube know what it has to show to you guys as viewers again
machine learning is in play in full-swing guys
it may be Instagram it may be Skype it may be Twitter or Vimeo and so much more
machine learning is there everywhere guys and then coming to video
surveillance as well so instead of manually tracking let’s say your traffic
violations or whatever it is machine learning can pretty much be implemented
and it is already implemented throughout the world even in India for example
where pretty much you know computers come to know whenever you know you cross
a signal when the red when the red light is on and then it can click a picture
and you know you can be fined for doing that as well guys so lots of other bases
where when you talk about you know chat BOTS such as Siri and Alex and so many
more guys so this brings us quickly to check out some of the interesting facts
about machine learning guys oh well you should know that you know every single
day there is your millions and millions of gigabytes of data coming through with
respect to every big fortune 500 organizations we have so if that is the
case then you need to know this as well in in pretty much in 2019 until 2019 90%
of the entire world’s data which was generated in the last two years right so
it was actually made use into apps it was pretty much put along to use with
devices as well and this only happened because there were machines which could
understand what the data is well on a simple on a simple note it feels that you
know machines do understand data well it actually doesn’t unless you teach it to
understand which is what machine learning does guys so this brings our so
the next thing which is data mining well data mining is to pretty much hunt
around think about mining literally you know hunting around a website and
pulling some useful and required information which you can use later
right so this is a manual activity to be honest with you guys but then with
machine learning then pretty much you know yeah you can go about automating
this entire data mining process and this can pretty much you know go about being
scaled to the future or requirements as well and then
brings us to the human operation well you might think okay so if machine
learning is that good then why do we still need humans right well you
actually need humans to do the teaching you have to you know provide a context
to the machines you have to set what are the parameters of operation how the
machine has to work and then you should analyze if the machine is learning and
how you can go about improving the machine as well guess so
that pretty much sums up human intelligence with machine learning as
well and this brings us to the power of machines well there are some things
machines can do faster and better than human beings and I’m sure you guys will
agree right so what do you think about can you think about you know two things
which machines can do better faster or than human being it might be anything
like you know heavy lifting or pretty much you know anything again you know
going underwater at huge depths and whatnot so tell me two things guys as
you all pretty much know by now we love you know a comment section filled with
interaction and you guys love it more than we do as well so write to the
comment section and let’s let’s have a talk there guys and then pretty much
machine learning as I told you with the power of machines there are some things
which are humanly impossible to see and to do especially with respect to data
because again data is this messy unruly thing which is pretty much driving the
world today so pretty much using machine learning with the power of humans making
adjustment to these unruly entities we can pretty much you know achieve what is
humanly impossible to achieve guys and then this brings us to architecture as
well with respect to architecture again architects are already making use of
machine learning to make better building infrastructure function because at the
end of the day they know that that whatever they’re building can function
better it can pretty much you know give you a better experience because you
might think about just you know putting a building out of nowhere if there are
any architects in the comment section any viewers you guys will pretty much
relate to this so whenever a new city is planned or whenever an existing city has
to be more efficient especially you know there was a documentary about the place
like Singapore as well so all the skyscrapers as Singapore has put through
our aerodynamic in fact so they pretty much know since it’s a tropical country
there are winds coming in from one particular day
so they want that entire went through go through or the entire entirety of
Singapore and not just end of the coast because there are big buildings there so
all of this is extremely well planned and all of this can be done and better
with the help of machine learning guys I just gave you a very small example but
then making sure the living floor space is maximized for every flat or house and
so much more as well and then coming to the biggest thing which I am the most
you know which I am a huge personal fan of is basically CAD guys so it’s
basically computer aided detection what what is the biggest achievement in 2019
in my opinion is pretty much we have in the world of machine learning pretty
much we have come to a stage where we can give you a 52% chance of spotting
breast cancer cells one year before the patient is actually diagnosed of cancer
guys one year so you get 365 whole days to check have a better insight of this
thing called cants cancer guys I mean it’s it’s surely a sad thing but then
with the use of computers we can give you a 52% chance which is better than
not having a chance for another year right so when you think about it this is
something you know it is something so deeper to something it is something
wonderful in my opinion bringing medicine and computer science together
guys so this quickly brings us to of the types of machine learning that there are
so guys we have three types of machine learning mainly one is the supervised
learning the second one is the unsupervised learning and then we have
reinforcement learning so let me give you a quick example of what supervised
learning is well think about variables as independent variables and dependent
variables as the name suggests an independent variable can function on its
own without being depending on anything but then a dependent variable will be
dependent on a factor in the particular equation so in this example what we’re
trying to find out is we’re trying to have the gender if it’s a girl or a boy
and we’re trying to see if the gender has anything to do with the kids you
know boy or girl passing an exam or failing an exam so pretty much in this
particular case our independent variable is gender because what we’re trying to
map is are the outcome with respect to this gender and the outcome
is if the student passed or failed so basically this will give us a function
where we can try to see if you know if there are logical chances and
possibilities of a girl or a boy passing more or if it’s the other wise as well
so it is pretty much as simple as that guys and then coming to unsupervised
learning Oh with the supervised learning there was something called as class
labels let me quickly go back so you can see that girl and boy right so the
computer or you know the machine that you’re trying to train already knows
that it’s a girl already knows that it’s a boy
this is something called as label because we are labeling that particular
identity to be a girl and a boy but then with respect to unsupervised learning
there is no class labeling at all done so the machines don’t know what they’re
particularly seeing unless they actually see it after being mapped and tested so
in this particular example consider that we are trying to achieve machine
learning right now so what we basically do is we send a couple of images of
birds and fishes to the machine we are trained as humans to understand and we
are taught in schools and pretty much you know our anger ages what a bird is
and what are fishes but then the Machine doesn’t know it just sees it as images
that’s all it has no idea what you are you showing it doesn’t know if you’re
showing a fish a bird a lion or dinosaur or whatever so in that particular case
you’re gonna run some algorithms and the machine is going to learn saying what a
bird is you know what a fish is and so much more and at the end end of it
unsupervised learning we have something called as clustering where we pretty
much split all these relevant data into separate sub structures called as
clusters and then you can see on the rightmost green example where we have
you know clustered out all these fishes separately and clustered out all these
birds separately as well so the machine has learned without a label to pretty
much go on to separate the fish separate the birds and so much more guys so this
brings us to reinforcement learning and in terms of reinforcement learning
basically you can think of any games that you play in this particular example
I have taken the very famous pac-man because again this was a childhood dream
a childhood best game for a lot of people that I know and then here as well
so pretty much you just have to go through and eat all the yellow dots
which are without touching the ghost and if you
if you touch the ghost then pretty much you’re dead so you can teach a machine
to do this if you if you make the machine to go touch a ghost it knows it
is not supposed to so it will pretty much not go to its end or whatever you
say goal but the goal here is to eat everything so it is gonna backtrack and
it is going to find another way since that it is not gonna touch that goes
it’s basically called as it rewards guys and the person who controls this pac-man
moving is called as an agent I have covered an in-depth tutorial and in the
in depth video on our YouTube very YouTube channel with the name machine
learning algorithms where we have actually covered this in a bit of detail
guys so make sure you check that out after viewing this video and then coming
to the actual learning process well let us consider a very simple scenario which
I would like to walk you through guys so consider this situation where we are
actually multiplying something right so the data for our multiplication is X is
equal to 1 and X is equal to 2 and then we have two cases where we are
multiplying both of these by 2 so 1 multiplied by 2 gives us 2 which we
already know and 2 multiplied by 2 which is already 4 as you can see on the
screen we already have the answer to be 2 & 4 but then now let us make a machine
do it so basically what this machine tries to do it tries to learn what’s
happening so it sees the data it tries to see if there are any rules it can
apply on the spot killer data to get this answer so this particular data what
should it be multiplied by to give you this answer is what the Machine figures
out so it realizes hey if I multiply 1 with 2 I’m gonna get this 2 and then if
I multiply this 2 with another two I’m gonna get this 4 so this multiplication
into 2 here and and into 2 here is what the Machine does and this particular way
of understanding rules and learning is the reason why we call it as machine
learning guys it is as simple as that so it will actually try different things
it’s going to try 1 into 3 1 into 4 1 into 5 and realize it’s not getting 2 so
it’s gonna try harder to come to 1 into 2 equal to 2 and then it is gonna
understand yes this is how it goes I’m gonna try the same thing for the next
example as well guys and coming to some terminologies that you might have heard
from your friends or you know anyone and in
machine learning deep learning or someone to data science as well guys
something called as data set a data set is basically a data example right in the
form of a list it can be ordered it can be unordered but this guy this is going
to contain all of the data that you need to do or to solve the problem guys think
of it as a problem and we have data to solve that problem a data set is
basically this data and then here’s something called as features so features
are the most simplest way I can tell you is it consider each column of the data
set to be a feature features are the very most important information which we
actually use through the data which is going to make us understand what the
problem is if we can understand the problem we can train the model to pretty
much you know you know get an answer to a problem and do some learning as well
so this brings us to what model is model basically we call something as a model
when we have performed machine learning algorithms and the machine has already
learned something and it gives us the output so whatever output we get through
this particular machine after the process of training is what we call as
model guys but then there are so many other terminologies as well we have
something called as loss we have something called fitting with something
called as generalizations we have hyper parameter or cipher parameter tuning and
so much more guys again we have in depth videos which cover these as well so to
keep this to the scope of our beginners and intermediate viewers pretty much
we’re gonna have to you know cut cut cut down on the complexity guys again
figures are because for the people who might have not heard it might be a
little too much and then this brings us to the process of machine learning guys
walk with me from the top to the bottom the first step in machine learning
pretty much is to go on to collect some sort of data through which we’re going
to be actually understanding what the problem is and we’re going to teach the
machine right so the first step is data collection and the second step is pretty
much to prepare this data because I can have as I already told you data is this
unruly mess pretty much and not as you know as nice as it sounds when we say
data so basically we need to format this data in a way the Machine understands we
need to engineer the way a way in which this data can be put into an optimal
format through which the Machine understand
what’s going on in the data and it can you know be ready for further processing
as well and then after data collection after data preparation we’re gonna do
this process called training this is the most important step in any machine
learning procedure because this is the actual step where your machine learning
algorithm learns it’s gonna take all the data and there are certain ways I’m
going to show you in the practical approach at the end of this video but
then there are certain wastes where your machine actually goes on to learn and
understand what’s going on in the data and pretty much process it as well guys
and after learning pretty much it’s just like going to school guys so if you have
learned something then you need to show that you have learnt it right so
consider the same evaluation like whatever exams you have in your schools
so it’s again we have to evaluate to test the model to see how well it has
actually learned if it has learned extremely well then well and good if it
is if it can get some you know get some more accurate or if it can get a little
more better than what it’s supposed to do then we can pretty much go on to this
particular phase what we call a stuning in this tuning again as the name
suggests we’re gonna be fine-tuning this model to extract the maximum performance
and pretty much you know go about using the full potential of whatever the model
has learned to solve the problem for us guys so inner just this is exactly what
the machine learning process is guys on that note I want you guys to head to the
comment section and do let me know what you think about this and if you guys do
order note downs in case you have 30 seconds I’ll just have a quick cup of
water I’ll just be back in 30 seconds guys so I’m bye guys I hope you guys pretty
much took away a lot from that particular slide as well and this brings
us to the exciting part where I’m going to show your demo which you can pretty
much you know perform after going about you know learning machine learning which
I’m going to just guide you after this particular demo guys so basically here
we have this is what machine learning code looks like if you think that you
know it looks like a lot to be honest with you it is not a lot case this is a
very simple model where you know this is a very interesting data set as well this
is the heart disease prediction data said well what are we trying to do is we
are trying to use a machine learning to see if we can you know predict if a
heart attack or any other heart disease can be prevented or what is the chances
that this heart disease can be shown as well guys so the first step into
executing all of these quarters to import all of the libraries guys we have
found us to work with data we have numpy to work with numerical computations you
have matplotlib or to show us graphs we have Sky kettle on a scale on basically
to again work with or take care all of our machine learning requirements and
basically this is all Python code that you see on your screen guys and we are
running this code on google collab guys so google collab is pretty much a you
know a jupiter notebook which is hosted on the google cloud platform and it is
free for all as well guys so basically you wouldn’t have to carry your code
everywhere you go and you just have to sit anywhere any cafe you want your home
your office and you can write the code and it’ll be there so the second step
after importing all the the form as we need is pretty much to choose our data
set because this data set is what we’ll be using guys it’s a file called as
heart dot CSV it’s a CSV file because it’s an excel file with all the rows and
columns of data which I’ll just show it to you and after uploading that we
pretty much are going to use pandas and convert it and read it and we can
actually read the file which which in our case is hard dot CSV guys and after
that we can actually print the data said to see you know what what values we have
what it looks like we have age of the people and we have sex guys if it’s a
zero it’s male if it that’s female or the either way so
basically it’s a binary number right so sex is male or female so in that case we
can use it as well and then you can check out the blood
pressure the cholesterol there are so many things that you can see guys there
as a target values that you need to be you know you need to achieve if
everything is normal and if something is out of place as well and then again
we’re gonna check out how many entries we have as well guys so we have age or
sex pretty much you know we have pulse wave cholesterol we have we have many
details out here guys so coming to what you you must be concerned in this
particular data example is that there are 303 non-null rows so there are 303
valued rows in all of these data columns that you see guys so let us see a
statistical description of what it might look like so again with respect to
statistical or description we can get the count of how many values we have
which what we just saw we can find the mean age you know we can find the we can
find the mean cholesterol and there is so much more we can find the standard
deviation the minimum value 25 percent deviation fifty percent in the normal
distribution seventy-five percent which is the first distribution and then we
can find the maximum value as well guys so this is just pretty much statistics
and this is gonna give us a better idea again to teach our model what better it
can learn guys and then there might be some null values that you might have to
remove as well as you can see it says it’s 303 normal values everywhere so
there are zero null values in a particular case so null value is
basically referred to zeros in a particular case if you have some values
as 0 so it is not useful for you it is not useful for the data nor is it useful
for the model unless it is something which is relevant so we usually tend to
find whatever value is 0 and we remove it in terms of machine learning concepts
guys and then coming to histograms we’re gonna plot to see what the data looks
like right so what you see on the x-axis is the age of the people and what you
see on the y-axis is the frequency of occurrence case so with respect to 32
you know 40-year old we have about 10-15 people there from 40 to 50 we have we
have lots of people you have 40 people the maximum people we have are above 60
years old are around 60 or sold if you can see you guys so I’m sorry about that
so basic thing that you need to map is what
your x-axis looks like when you you know put it right versus y-axis so at the age
sixty you can check out that you know there are pretty much over 60 people
with this particular age and then it keeps dropping from there after as well
guess it’s as simple as that and then coming to the next part is where we
split our data into what we call as training data and testing data with
respect to the training data this data is where the Machine uses to learn and
test data is what we later used to see if the Machine understood anything as I
have told you in the previous set the verification stage is done using the
test data guys so as soon as I go on to run this it pretty much splits or raw
whatever data that we we’ve shown you’re into pretty much tests and know what
training data guys and then after that we’re going to do this process called as
fitting model and fitting the model and in this particular case we’re going to
be using a decision tree classifier to classify our values as our particular
case case then we’re going to be using something called as Gini index here as
well and if you do not know any of these concepts or any of these words guys make
sure to stick to the end of the video I’ll be guiding you through your
fast-track way on how you can learn all of these master concepts and gain a
certificate in the same as well guys so after that pretty much you’re gonna
perform actual predictions of what we can actually what the model actually
learnt so we ever we have a score of 70 point three three percentage in
detection of if there is an anomaly in the heart conditions or if there is an
occurrence of disease guys so instead of just having zero percentage of you know
being blank about what goes around we have used machine learning to give you a
70% accuracy in detection with respect to the data that you know what something
is wrong giving all of the conditions set oh it has been put through all of
the features is the exact word that it has used to learn guys and after that we
are print something called as confusion matrix and then um guys do not be
confused you know looking at the confusion matters it is actually a
pretty simple thing to read again if something called as a false negatives
false positives true positives and true negatives which we basically map onto
each of these block and then we use something called does this heat map here
that you can see on the right side to the particular number associated with it
and we can compare of what’s going on in the matrix
what is the value base so but then at this point of time you need to know that
this particular score is the most important thing here as well because you
will come to know that you have a 70% accuracy and your model is training well
guys and then you can have better accuracy numbers as well this is a very
small demo to show you what this particular thing can do but then we have
you can go up to 80 85 % accuracy and more as well and that is so much more
when you talk about data in the world of machine learning guys so this brings us
to my final thoughts on this particular thing so I love this particular code
which is by dr. Pedro or Domingo from the University of Washington
so dr. Pedro says machine learning cannot learn something from nothing so
you have to train the machine to learn and then he says that you know what it
learns is pretty much it gets it learns to get more from less so guys give that
code another read machine learning cannot get something from nothing
what it does is it gets more from what’s less so that’s an amazing quote and
every time I read that again I pretty much sure Oh makes me love machine
learning more than how much I already have and then we already know that
having a machine learning job is one of the most trendiest job in today’s world
guys and I’m sure you guys already know this as well so this brings me to the
concept of further learning as well in further learning guys as you might have
already known you know an investment in knowledge always pays the best interest
ever guys so make sure you invest in your knowledge because again you know
investing in your knowledge as the code says by Benjamin Franklin is an amazing
code and I pretty much champion this code and all of my videos has my dear
viewers you will love this coat as well guys
so again if you need any more info on any of these particular concepts that we
have learnt or anything else make sure you head to our youtube channel where we
have comprehensive tutorials videos from subject matter experts our software
architects and so much more our entire content team is pretty much putting
content out day and night for you guys and then if you’re not much of a video
person we have an entire in telepath blog channel set up where you can sit
and read blogs again written by a subject matter experts architects and so
many more people in collaboration with Fortune 500 companies guys so pretty
much you can check out our blocks and our YouTube page and you can pretty much
say Cod are in telepath community as well a communities where you know we
have people they are arranging from the very low ages school ages all the way
till senior citizens and we come together and we have very nice IT
discussions non IT discussed and some time as well we talk about Python we
talk about R we talk about tableau we talk about so many things guys so make
sure you head to Google and search for in telepath community and you’ll be
amazed pretty much at what you can find when you’re pretty much surrounded by
the people who love data as well guys so this pretty much brings me to the part
where you know you can pretty much have to guide you through of how you can
become a machine learning engineer expert as well guys so again as soon as
I come to be in telepaths Webb said you know we pretty much giving you free
self-paced courses as well guys make sure you enter your email make sure you
enter your phone numbers your and hit free courses and pretty much you will be
given a free self-paced course as well so this brings us to our the machine
learning online course guys so this particular program from in telepaths is
again very well reviewed it’s a five star avid course from all of our
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team which stands by and we can give you I love said lifetime access and lifetime
support which is another amazing thing which all of our learners like guys so
there are some questions that you might have answering you know what will you
learn who should take up the course what are the prerequisites why should you
learn machine learning and so much more guys if you head to the in telepath
website and if you type in machine learning certification you can pretty
much or get to get the answers to all of your questions you are as well but then
to give you a quick overview of the course content we can pretty much
you will be learning everything from the introduction of machine learning to
supervised learning unsupervised learning
you-you’d beach you can check out the hands-on exercises you will be doing in
every module as well so you’ll be checking our classification regression
decision tree random forests naive by us something called as SVM’s which is
basically support vector machines you’ll be learning on star unsupervised
learning as I’ve told you NLP which is natural language processing text mining
and again whatever it a hands-on which is which goes on to that particular
module deep learning is well and then something called as TSA which is
basically time series analysis where you can do a lot of Twitter sentiment
analysis ARIMA models sentiment analysis again not only just Python you can do it
in a lot of places as well univariate time series models multivariate time
series models and so much more and at the end of it you can pretty much
you know have a lot of case studies and you need to walk to and precisely there
are you know six case studies out here that you’ll be learning decision pre
insurance cost prediction diabetes classification random forests or PCA
which is another very important concept which is asked in a lot of jobs
principal component analysis k-means clustering again we just things are
clustering with respect to our unsupervised learning right so it’s that
and then we have a lot of projects that you’ll be working on customer churning
classification movie recommendations and all of these guys you will get a lot of
data if you just head to the in telepaths website and again guys if you
want to check out sample videos we do have our code sample videos up for you
as well and at the end of it you will be given a certificate from intelli path
which looks something like this and this is pretty much you know accepted which
is highly renowned and recognized in more than 100 MNCs like mu sigma
cognizant erickson sony cisco hike service and chartered IBM Infosys
Genpact takes over TCS and much more guys you can check out the reviews of
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so if you have any more questions give me a second I’ll just guide you through
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