Machine Learning is a subset of artificial intelligence that allows computers to learn without being explicitly programmed. It uses algorithms to recognize patterns in data and make predictions. The document discusses common machine learning algorithms like linear regression, logistic regression, decision trees, and k-means clustering. It also provides examples of machine learning applications such as face detection, speech recognition, fraud detection, and smart cars. Machine learning is expected to have an increasingly important role in the future.

1. Machine Learning
By Rajat Kumar

2. Overview
Introduction
What is the need ?
Machine Learning Algorithms
Applications
Conclusions

3. Introduction
Thanks to the likes of Google, Amazon, and Facebook,
the terms artificial intelligence (AI) and
machine learning have become much more
widespread than ever before.They are
often used interchangeably and promise all
sorts from smarter home appliances to robots
taking our jobs.
But while AI and machine learning are very much
related, they are not quite the same thing.

4. Introduction continued…
• What is Artificial Intelligence ?
Artificial intelligence (AI) is intelligence exhibited by machines.
In computer science, the field of AI research defines itself as the study of
"intelligent agents": any device that perceives its environment and takes actions
that maximize its chance of success at some goal.
• What is Machine Learning ?
Machine Learning is a subset of Artificial Intelligence that, according to Arthur
Samuel in 1959, gives "computers the ability to learn without being explicitly
programmed.”
It evolved from the study of pattern recognition and computational learning
theory in artificial intelligence.

5. Introduction continued…
• Difference between AI and ML:
AI is basically the intelligence – how we make machines intelligent, while machine
learning is the implementation of the compute methods that support it.
AI is the science and machine learning is the algorithm that make the machines
smarter.
So , Machine Learning is an approach to achieve Artificial Intelligence.
• Definition of Machine Learning :
In 1997, Tom Mitchell gave a “well-posed” definition of Machine Learning : “A
computer program is said to learn from experience E with respect to some task T and
some performance measure P, if its performance onT, as measured by P, improves
with experience E.”

6. Introduction continued…
• So what is actually Machine Learning ?
The machine learning approach, is to say, instead of writing each program by hand
for each specific task, for particular task, we collect a lot of examples, and specify
the correct output for given input. A machine learning algorithm then takes these
examples and produces a program that does the job.
The program produced by the linear algorithm may look very different from the
typical handwritten program. For example, it might contain millions of numbers
about how you weight different kinds of evidence. If we do it right, the program
should work for new cases just as well as the ones it's trained on. And if the data
changes, we should be able to change the program runs very easily by retraining it
on the new data.

7. Introduction continued…
• Examples of Machine Learning :
Some examples of the things that are best done by using a learning algorithm are
recognizing patterns, so for example objects in real scenes, or the identities or
expressions of people's faces, or spoken words. Predicting future stock prices or
currency exchange rates are also possible by using ML.
Other examples of machine learning problems include, “Is this cancer?”, “What is
the market value of this house?”, “Which of these people are good friends with
each other?”, “Will this rocket engine explode on take off?”, “Will this person like
this movie?”, “Who is this?”, “What did you say?”, and “How do you fly this
thing?”.
All of these problems are excellent targets for an ML project, and in fact ML has been
applied to each of them with great success.

8. What is the need ?
ML solves problems that cannot be solved by numerical means
alone.
Machine learning is needed for tasks that are too complex for
humans to code directly. Some tasks are so complex that it is
impractical, if not impossible, for humans to work out all of the
nuances and code for them explicitly. So instead, we provide a
large amount of data to a machine learning algorithm and let the
algorithm work it out by exploring that data and searching for a
model that will achieve what the programmers have set it out to
achieve.

9. Algorithms of ML
There are 3 types of Machine Learning techniques :
• Supervised learning:The computer is presented with example inputs and
their desired outputs, given by a "teacher", and the goal is to learn a
general rule that maps inputs to outputs.
• Unsupervised learning: No labels are given to the learning algorithm,
leaving it on its own to find structure in its input. Unsupervised learning
can be a goal in itself (discovering hidden patterns in data) or a means
towards an end.
• Reinforcement learning: A computer program interacts with a dynamic
environment in which it must perform a certain goal (such as driving a
vehicle or playing a game against an opponent).The program is provided
feedback in terms of rewards and punishments as it navigates its problem
space.

10. Algorithms
All these 3 techniques are used in various Machine Learning Algorithms .
We will discuss some of them :
1. Linear Regression :
In simple linear regression, we predict scores on one variable from the
scores on a second variable.
Linear regression consists of finding the best-fitting straight line through
the points.
The best-fitting line is called a regression line.

49. Algorithms
2. Logistic Regression :
Logistic Regression is used to estimate discrete values (usually binary
values like 0/1) from a set of independent variables. It helps predict the
probability of an event by fitting data to a logit function.

62. Algorithms
3. DecisionTree :
One of the most popular machine learning algorithms in use today, this is a
supervised learning algorithm that is used for classifying problems.
It works well classifying for both categorical and continuous dependent
variables. In this algorithm, we split the population into two or more
homogeneous sets based on the most significant attributes/ independent
variables.

74. Algorithms
4. K-Means Clustering :
This is an unsupervised algorithm which solves clustering problems. Data sets are
classified into a particular number of clusters (let's call that number K) in such a way
that all the data points within a cluster are homogenous, and heterogeneous from
the data in other clusters.
How K-means forms clusters:
• The K-means algorithm picks k number of points, called centroids, for each cluster
• Each data point forms a cluster with the closest centroids i.e. k clusters.
• It now creates new centroids, based on the existing cluster members.
• With these new centroids, the closest distance for each data point is determined.
This process is repeated until the centroids do not change.

96. Applications
There are numerous applications of machine learning. It's actually hard to realize
how much machine learning has achieved in real world applications.
Machine learning is generally just a way of fine tuning a system with tunable
parameters. It is a way of making a system better with examples, usually in a
supervised or unsupervised manner.
Machine learning is normally applied in the offline training phase .Thus machine
learning is used to improve the following applications :-
1. Face detection:The face detection feature in mobile cameras is an example of
what machine learning can do. Cameras can automatically snap a photo when
someone smiles more accurately now than ever before
because of advances in machine learning algorithms.

97. Applications
2. Face recognition:This is where a computer
program can identify an individual from a photo.
Facebook uses this feature for automatically
tagging people in photos where they appear.
Advances in machine learning means more accurate
auto-face tagging soft wares .
3. Image classification: A good example is the
application of deep learning to improve image
classification or image categorization in apps such
as Google photos. Google photos would not be
possible without advances in deep learning.

98. Applications
4. Speech recognition: Another good example is Google now. Improvements in
speech recognition systems has been made possible by machine learning
specifically deep learning.

99. Applications
5. Google: Google defines itself as a machine learning company now. It is also a
leader in this area because machine learning is a very important component to
it's core advertising and search businesses. It applies machine learning to
improve search results and search suggestions.

101. Applications
6. Fraud Detection: Machine learning is getting better and better at spotting
potential cases of fraud across many different fields. PayPal, for example, is
using machine learning to fight money laundering.The company has tools that
compare millions of transactions and can precisely distinguish between
legitimate and fraudulent transactions between buyers and sellers.
7. Anti-spam: Machine learning is also used to train better anti-spam software
systems.
8. Weather forecast: Machine learning is applied in weather forecasting software
to improve the quality of the forecast.

102. Applications
9. Smart Cars: IBM recently surveyed top auto executives, and 74% expected that
we would see smart cars on the road by 2025. A smart car would not only
integrate into the Internet ofThings, but also learn about its owner and its
environment. It might adjust the internal settings — temperature, audio, seat
position, etc. — automatically based on the driver, report and even fix problems
itself, drive itself, and offer real time advice about traffic and road conditions.
10.Healthcare: It is used to diagnose deadly diseases (e.g. cancer) based on the
symptoms of patients and tallying them with the past data of similar kind of
patients.

103. Conclusion
We have a simple overview of some techniques and algorithms in
machine learning. Furthermore, there are more and more
techniques that apply machine learning as a solution.
In the future, machine learning will play an important role in our
daily life.
Clearly, Machine Learning is an incredibly powerful tool. In the
coming years, it promises to help solve some of our most pressing
problems, as well as open up whole new worlds of opportunity.