Hey guys welcome to the session by Intellipaat
Now you guys must have come across the term machine learning and you must be
wondering what exactly it does. So in today's session will be covering just
that so that you can start your journey in the field of machine learning so
we'll understand the concepts of machine learning with this little example over
here so what do you see over you what is it exactly it's a car isn't it and how
about this well, this again is a car and this well this too is a car now how do
you know all of these are cards well as a kid you might have come across a
picture of a car and you would have been told by your kindergarten teacher
or your parents that this is a car and your brain learned that anything which
looks like that is a car and that is how a brain
functions but what about a machine, now if I take this image of a car and fill
it to a machine will it be able to identify that it's a car so this is
where machine learning comes in so what I'll do is I'll take all of these images
of a car and keep on feeding them to the machine until it learns all the features
associated with this car and once it learns all the features associated with
this car I will give it new data or the test data to determine how well it has
loaded or in other words first I'll feed the machine training data so that it can
extract all the features from the training data and once the training is
done I will give it test data to determine how well the learning has been
done and this is the underlying concept of machine learning
so now that you've understood what exactly is machine learning let's have a
look at the categories of machine learning so machine learning can be
divided into supervised learning and unsupervised learning. So let's start
with supervised learning, so in supervised learning we have input
variables and output variables and these input variables are denoted with X and
the output variables are denoted with Y and we try to understand how does
Y vary with X or in other words we have dependent variables and independent
variables and we try to determine how does a dependent variable change with
respect to these independent variables now again the supervised learning can be
divided into regression and classification. So let's start with
classification so in classification the output variable is of categorical in
nature so let's take this example to understand it better over here we have
gender of the student and the result of the student so gender is the independent
variable and result is the dependent variable and we're trying to determine
where the student will pass the exam or not on the basis of the gender of the
student or in other words we are basically trying to classify the data so
next we have regression. So in regression the output variable is a continuous
numerical so let's take this example so over here we are trying to predict
the prices of Apple which is basically a continuous numerical so guys this is how
supervised learning algorithms work next we'll head on to unsupervised learning
so in unsurprising we have input data with no class labels and we try to build
a model to understand the underlying structure of the data so let's take this
example over here. So we have this input data with no class labels and this input
data comprises of fish inputs I will take this input data and I'll build an
unsupervised learning model on top of this input data so this will give me two
clusters so the first cluster of comprised of all the fish and the
second cluster comprised of all the birds now you guys need to keep in mind
that even though there were no class labels this unsurprising model was able
to divide this data into two clusters and this clustering has been done on the
basis of similarity of characteristics so in the first cluster all of the
values are similar. All of them are fish and the second
cluster all of the values are similar so this time
all of them are birds so this is how an unsupervised learning model works. So
guys this was a brief intro to machine learning and do stay tuned to
Intellipaat's YouTube channel for more such informative videos.