Warm welcome to each and every one of you for today's session
bought to you by Intellipaat. So, Machine Learning is a subfield of artificial
intelligence where data is being fed into machines to make some insightful
decisions. So broadly, Machine Learning is divided
into three: (1) supervised learning, (2) unsupervised learning, and (3)
reinforcement learning. So, in today's session, we'll be largely focusing on all
these three terms, what they are, and what are the differences between them. So,
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Intelligence. So, without any further delays, let's get started.
Machine Learning is subcategorized 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 labeled. Let's say we have a
bunch of fruits and each fruit is tagged with a 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 a new image of Apple and the machine
predicts that there is a 97% 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 other examples. So, let's say you live in
a gated housing society, and your society has separate dustbins for different
types of wastes. 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 wastes into different categories. So the
classification is the process of assigning a class label to a particular
item. In this example, we are assigning the labels paper, metal,
plastic, and so on, to different types of wastes. 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. 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.
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 a decision tree to find out if the person is fit or not.
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,
although a 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 our output variable is of
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 the spam email, so phrases like lottery, or you won, or 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 email. 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 emails
constantly, or several of the users' messages have already been marked as
spam, then in that case, their email will be blocked entirely. So, this brings us
the use of blacklist. Just a quick info guys: In case you are interested in an
end-to-end certification course in Artificial Intelligence, please do check
out Intellipaat's Artificial Intelligence Engineers Master's Course
that is co-created in association with IBM. So, this course provides all the
required skills to become a successful Artificial Intelligence Engineer. The
information about the same is available in the description below. So now, let's
get back to the session. 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 users' 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, so the machine checks 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 in 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. So when it comes to
unsupervised learning, the input data doesn't have any labels associated with it. The
machine understands the underlying structure of the data to identify
similar patterns, and the data which is similar in nature is grouped together. So,
here even though there are no labels, the machine understands that all the apples
are similar in nature, and thus they are grouped together.
Same is the case with bananas and oranges. So this is the concept of
unsupervised learning. Now let's go through an unsupervised machine learning algorithm called
as K-means. So, the aim of K-means is to group similar data points into a single
cluster. So, there must be high intra-cluster similarity and low inter-cluster
similarity, that is, all the data points within a cluster should be as similar as
possible, and all the data points between two different clusters should be as
dissimilar as possible, or in other words, all the points inside cluster A should
be very similar to each other, but when we compare a point from cluster B with a
point from cluster C, then they would be very dissimilar to each other. In
K-means clustering, 'K' denotes the number of clusters to be formed. So in this
picture over here, the value of K is 3, and hence 3 clusters are formed. Now
let's go through a use case of unsupervised learning. Netflix recommendation is a perfect use
case of unsupervised learning. So, more than 80% of the TV shows that
you watch on Netflix are discovered through the platform's recommendation
system. It means the majority of what you watch on Netflix is the result of
decisions made by a mysterious black box of an algorithm. So, let me just give you
a brief of how it works. Well, Netflix uses Machine Learning algorithm to
recommend you a list of movies and shows that you might not have initially chosen.
To do this, it looks through a thread within the content rather than relying
on new joiners to make a prediction. This explains how 1 in 8 people who watch
Marvel shows are completely new to comic book-based stuff on Netflix. Finally, there's
reinforcement learning. So, in reinforcement learning, the algorithm
learns through a system of rewards and punishment, and the goal here is to
maximize the total reward. Let's take this example to understand reinforcement
learning. So here we have a self-driving car which is supposed to reach its
destination without hitting any barricades. So here, the self-driving car
is the agent, and the road is the environment. Here, the car takes an action
and goes straight but when it goes straight, it directly hits the barricade. Now since
the car has taken a wrong action. it will be punished. So, the car realizes that
going straight is wrong and it has to go right. So when it goes right, it will be
given a reward. So this process continues and the car learns how to drive by
itself without hitting any barricades. So the obvious application of reinforcement
learning is a self-driving car. A recent study has shown that over 90% of
road accidents are caused by human error. So to err is human, but behind the wheel,
mistakes are often more catastrophic. Accidents have led to a massive amount
of unnecessary deaths. So, this is where self-driving cars come
into the picture. So, trust me, the autonomous car or the self-driving
car is much safer than a human-driven car.
They are unaffected by factors like human fatigue or dullness, and this makes
them very safe and self-driving cars are always attentive observing their
environment and scanning multiple directions it would be difficult to make
a move that the car is not anticipating so how does a self-driving car work well
self-driving cars mainly rely on three technologies IOT sensors IOT
connectivity and software algorithm there are many types of sensors
available for our self-driving car for example sensors for blind spot
monitoring forward collision warning radar and so on so all of these IOT
sensors work together to help in navigation of the self-driving car next
is the IOT connectivity self-driving cars use cloud computing to act
on traffic data weather maps adjacent cars and so on this helps the car to
monitor its surroundings better and make informed decisions and next is the
software algorithm all the data is the car collects needs to be analyzed to
determine the best course of action and this is the main function or the control
algorithm and this is where reinforcement learning comes into the
picture this is the most complex power of the self-driving cars since it has to
make decisions flawlessly so in today's world the most famous self-driving cars
are those from Tesla and Google so Tesla cars work by analyzing their environment
by using a software system known as autopilot so autopilot uses high-tech
cameras to view and collect data from their environment so it's the same as
what we do with our eyes and autopilot uses this data to make decisions during
navigation now let's quickly head onto the quiz so this is a first question who
here Amazon product recommendation uses which of the following machine learning
techniques sepoys learning unsupervised learning or reinforcement learning so
guys what do you think is the answer so whatever you think is right do put down
in the comment box just a quick info guys in case you are
interested in an end-to-end certification course in artificial
intelligence please do check out intelli Pat's
artificial intelligence engineers master's course that is co-created in
association with IBM so this course provides all the required skills to
become a successful artificial intelligence engineer the information
about the same is available in the description below so guys this brings us
to the end of the session in case you have any clarifications concerns
difficulties please post your comments below we will reach out to you
immediately also guys don't forget to subscribe to intelli Pat's YouTube
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all