Hey guys.
Welcome to the session by Intellipaat.
Now you guys would have come across the
terms artificial intelligence, machine learning
and deep learning and numerous questions would
have popped into your head.
So today's session would help you to understand
the difference between these three terms.
So let's have a quick glance at the agenda.
We'll start off by understanding the difference
between artificial intelligence, machine learning
and deep learning and then we'll understand
the different types of machine learning algorithms.
After that we will understand the limitation
of machine learning.
Going ahead we'll see how does deep learning
would come this limitation of machine learning.
Following which we will look at the working mechanism of deep learning.
And finally there'll be a quiz
to recap what we've learned in today's session.
So put down all of your answers in the comments section.
to know if you answered correctly.
And also you can put down your queries.
We would love to help you out.
So without much delay.
Let's start off with the class.
So I'll start off by asking you a very simple
question.
So tell me what is it that makes humans intelligent?
Well, we as humans can think, learn and make
decisions.
And that is what makes us intelligent.
Now imagine if machines can show human like
intelligence. A machine which can think and
make decisions like humans.
Now it is truly amazing, isn't it. So artificial
intelligence is basically that field of computer
science which emphasizes on the creation of
intelligent machines which can work and react
like humans.
So now that we know what artificial intelligence
is, let's see where do machine learning and
deep learning fed in. So you can consider
artificial intelligence to be the broader umbrella
and machine learning and deep learning to
be a subset of it.
You can also say that machine learning and
deep learning are a means to achieve artificial
intelligence.
Now let's see what machine learning is? So, machine
learning is basically the subset of artificial
intelligence where we teach a machine how to
make decisions with the help of input data.
So we'll understand machine learning with
this little example.
So what do you see over hear? What is this exactly?
Well it's a bird
And what about this?
This again is a bird and this well this too
is a bird.
Now how do you do all of these are birds.
Well as a kid, you might have come across a picture of a bird and you would have been
told by your kindergarten teacher or your
parents that this is a bird and your brain
learn that anything which looks like that
is a bird.
And that is how our brain functions.
But what about a machine?
Now if I take in this image of the bird and
feed it to a machine. Will it be able to identify
it as a bird?
So this is where machine learning comes in.
So what I'll do is I'll take all of these
images of the birds and keep on feeding
them to the machine until it learns all the
features associated with it.
And once it learns all the features associated
with it, I'll give it new data to determine
how well it has learned. Or
in other words, first I'll feed in training
data to the machine so that it can extract
or learn all the features associated with
the training data.
And once the learning is done, I'll give it
new data or the test data to determine how
well the learning is done and this is the
underlying concept of machine learning.
Now let's head onto deep learning.
So deep learning is the subset of machine
learning where we develop intelligent algorithms
which mimic human brain.
So now the question which arises over here is
how do we mimic human brain?
Well to answer that,
let me ask another question.
So what is a brain composed of?
Well our brain is primarily composed of neurons isn't it.
And these neurons send and receive electrochemical signals.
So we have a neuron over here and the electrochemical
signals are received from the dendrite.
The processing of these signals is done in
the cell body and the output of these input
signals is sent to other neurons to the axon
and if our task is to mimic human brain, all
you have to do is create artificial neurons
and these artificial neurons work the same
way as biological neurons. So to implement deep learning,
we'd have to create artificial neural networks
and these artificial neural networks companies
comprises of an input layer, hidden layer and then output layer.
So all of the inputs are received through
the input player and the processing is done
in the hidden layer
and the final output received through
the output layer.
And to sum it up artificial intelligence is
the broader umbrella.
Machine learning is the subset of artificial
intelligence and deep learning is the subset
of machine learning and machine learning and
deep learning are basically methods to
achieve artificial intelligence.
Now that we have understood the basic difference
between artificial intelligence, machine learning
and deep learning.
Let's go ahead and look at these subcategories
of machine learning.
So, Machine learning a subcategories into
supervised learning,
unsupervised Learning and reinforcement learning.
So let's start off with supervised learning.
In supervised learning,
we teach the machine using data which is
lebelled.
Let's say we have a bunch of fruits and each
fruit is tagged with the label.
So here, the machine learns that an apple
looks like this. A mango looks like this. A banana
looks like this and an orange
looks like this.
So once the training is done, the machine is
fed with new data or test data.
So here the machine is fed with the new
image of Apple and the machine predicts that
there is a 97 percent probability that this
is an apple.
Now supervised learning can again be divided
into classification and regression.
So classifying the input data is a very important
task in machine learning.
For example whether the mail is genuine or
spam. Whether the transaction is fraudulent or not.
And there are multiple examples.
So let's say you live in a gated housing society
and your society has separate dustbins for
different types of waste.
So one dustbin for paper waste. One for
metal waste. One for plastic waste and so on.
So now what you are basically doing over here
is classifying the waste into different categories.
So the classification is the process of assigning
a class label to a particular item.
And in this example, we are assigning the labels paper, metal, plastic and so one to different
types of waste.
And one thing to note in classification algorithms
is that the output variable is categorical in nature.
Let's take this example to understand it better.
So over here we have gender of the student
and result of the student. Here gender is the
input variable or the independent variable
and result is the output variable or the dependent  variable.
So we see that our output variable is categorical in nature.
That is, it has two categories either pass
or fail.
And here we are trying to determine whether the student will pass the exam or not on the
basis of the gender of the student.
So there are different classification algorithms
such as decision tree, random forest,
naive bayes, and support vector machine to
name a few.
So let's go through the concept of Decision
tree briefly.
Decision Tree as the name states is a tree
based classifier in machine learning. You
can consider it to be an upside down tree
where each node splits into its children based
on a condition.
So let's take this example to understand decision
tree better.
Here we are building our decision tree to
find out if the person is fit or not and
based on a series of test conditions,
we finally arrive at the leaf nodes and classify
the person to be fit or not.
So the next type of supervised learning algorithm
is regression.
So when it comes to regression, the output
variable or the dependent variable is
a continuous numerical.
So let's take this graph over here.
Here we have horsepower on the Y-axis and
miles per gallon on the X-axis.
Or in other words,
horsepower is the dependent variable
and miles per gallon is the independent variable.
So we see that the output variable is a continuous numerical.
And here we are trying to determine how does
the horsepower of the car vary with its MPG.
So this is the concept of regression.
Now let's see some of the use cases of supervised learning.
So first we have spam classifier.
So how do you think your mail is being classified
as whether it is spam or not.
This spam detection basically works on the
concept of filters.
So mainly there are two types of filters,
text filter and client filter.
The text filter works by using algorithm that
detects which words and phrases are often
used in this spam email.
So phrases like like lottery or you won or free bitcoin are often an immediate flag for removal by filters.
So next is the client filter.
As the name states,
client filter understands the client identity
and history to block malicious and annoying
spam e-mail.
So how does this client filter work.
Well this is done by looking at all the messages
of a certain user which he has sent out.
So if a user has sent out huge amount of e-
mails constantly
or several of the users messages have already been marked as spam then in that case their
e-mail will be blocked entirely.
So this brings us the use of blacklist.
So spam filters also include something known as blacklist.
So blacklisting is the process of adding known email addresses of a spammer to a list.
And this list prevents the user's messages
to be forwarded to someone else.
Now let's look at the next use case.
Fingerprint analysis.
So in fingerprint analysis, you save your
fingerprint in a machine as a record.
The machine takes the data and verifies
whether the data belongs to you or not.
So once the data is saved into the machine.
From next time whenever you put your fingerprint
on the machine.
The machine will scan through all of its user data.
And if the machine finds a match of your fingerprint
and its data,
then your fingerprint will be verified.
Now that we've completed the different
types of supervised learning algorithms, let's
go ahead and understand what is unsupervised learning.
