The document serves as an introductory overview of machine learning, detailing its definition as a branch of artificial intelligence that uses data and algorithms to make predictions. It outlines the differences between conventional programming and machine learning, describes the types of machine learning (supervised, unsupervised, and reinforcement learning), and provides real-life examples for each type. Additionally, it highlights the capabilities and limitations of machine learning in various applications such as virtual assistants, email filtering, and social media functionality.

1. Machine Learning Class XI CBSE
Basics of ML

2. What is Machine
Learning?
Machine Learning is a
branch of Artificial
Intelligence(AI) which
predicts results based on
incoming data.
It is based on the use of
data combined with
powerful algorithms to
train computers to learn on
their own, gradually
improving accuracy.
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3. How does it work?
Input Data
Analyze
Data
Find
Patterns
Predictions
Store
Feedback
Improve
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4. Conventional programming vs machine learning
Conventional programming and ML
coding both are computer programs, but
their approach and objective are
different.
Conventional
Programming
refers to any
manually created
program which
uses input data,
runs on a
computer and
produces the
output
In Machine
Learning (ML), the
input data and the
output data are fed
to an algorithm
(Machine learning
algorithm) to
create a program.
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5. Conventional Programming Approach
Step -1: Take input (Celsius)
Step-2: Apply the conversion formula:
Fahrenheit = Celsius * 1.8 + 32
Step -3: Print the Output (Fahrenheit)
Machine Learning Approach
Step 1: Feed lot many values in Celsius (i.e. -40, -10, 0, 8,
15, 22, 38)
Step -2: Feed corresponding Fahrenheit values (i.e. -40,
14, 32, 46, 59, 72, 100)
Step -3: Pass these 2 sets of values to Machine Learning
(ML) algorithm
Step- 4: Now you ask the ML program to predict
(convert) any other Celsius value to Fahrenheit, and
program will tell you the answer.
Problem : to convert Celsius scale to Fahrenheit scale
Conventional programming vs machine learning
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6. Types of Machine Learning
Machine learning is often divided into
three categories –
• Supervised,
• Unsupervised and
• Reinforcement learning.
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7. Supervised Learning
Supervised Learning occurs in the presence
of a supervisor or a teacher. We train the
machine with labeled data (i.e. some data is
already tagged with correct answer). It is
then compared to the learning . A supervised
learning algorithm learns from labelled
training data, and then becomes ready to
predict the outcomes for unforeseen data.
Step 1: You provide the system with data that
contains photos of apples and let it know that
these are apples. This is called labelled data.
Step 2: The model learns from the labelled data
and the next time you ask it to identify an apple, it
can do it easily.
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8. Real Life examples of Supervised Learning
Spam Filters Fraud Detection
Recommendation
systems eg Netflix
Speech recognition Image classification
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9. Unsupervised Learning
Unsupervised learning is a ML technique
where we don’t need to supply labelled data,
instead we allow the machine learning model
(algorithm) to discover the patterns on its
own. The task of the machine is to assemble
unsorted information according to
resemblances, patterns and variances without
any former training of data.
In this kind of learning, the machine is
restricted to find a hidden structure in the
unlabeled data without guidance or
supervision.
Step 1: You provide the system with a data that
contains photos of different kinds of fruits and ask
it to segregate it. Remember, in case of
unsupervised learning you don’t need to provide
labelled data.
Step 2: The system will look for patterns in the
data. Patterns like shape, colour and size and group
the fruits based on those attributes
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10. •Audience
segmentation.
•Customer persona
investigation.
•Anomaly detection
(for example, to
detect bot activity)
•Pattern recognition
(grouping images,
transcribing audio)
•Inventory
management (by
conversion activity or
by availability)
•Optical Character
recognition (including
handwriting
recognition)
Real Life examples of Unsupervised Learning
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11. Reinforcement Learning
In reinforcement learning, the machine is not
given examples of correct input-output pairs,
but a method is provided to the machine to
measure its performance in the form of a
reward.
Reinforcement learning methods resemble
how humans and animals learn, the machine
carries out numerous activities and gets
rewarded whenever it does something well.
In reinforcement learning, artificial intelligence
faces a game-like situation. The computer employs
trial and error to come up with a solution to the
problem. To get the machine to do what the
programmer wants, the artificial intelligence gets
either rewards or penalties for the actions it
performs. Its goal is to maximize the total reward.
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12. Self driving
cars
Automated
Robots
•Natural
Language
Processing
•Marketing
and
Advertising.
•Image
Processing.
•Gaming.
•Energy
Conservation
•Traffic
Control
Real Life examples of Reinforcement Learning
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14. What machine learning can do
1. Virtual Personal Assistant like Siri, Alexa, Google Home etc.
2. Predictions while commuting - like Traffic Forecasts on Google Maps.
3. Video Surveillance systems nowadays are powered by AI that makes it possible to detect crime
before they happen. They track unusual behavior of people like standing motionless for a long time,
stumbling, or napping on benches etc.
4. Social Media Services
 Facebook Friend Suggestion: Facebook continuously notices the friends that you connect
with, the profiles that you visit very often. On the basis of continuous learning, list of
Facebook users is suggested that you can become friends with.
 Face Recognition on Facebook: When you upload a picture of yourself with a friend it
checks the poses and projections in the picture, notices the unique features, and then
matches the same with the people in your friends list.
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15. What machine learning can do
5. Email spam and malware filtering - Emails are arranged according to some standards as per
email spam. Mail filtering manages received mails, detects and removes the ones holding malicious
codes such as virus, Trojan or malware.
6. Product recommendations - You often receive emails from similar merchandizers after you have
shopped online for a product. The products are either similar or matches your taste, it refines the
shopping experience.
7. Online Fraud Detection: Machine learning is lending its potential to make cyberspace a secure
place by tracking monetary frauds online.
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16. What Machine Learning cannot do?
• Any problems or questions which require social context will take longer for a machine to solve.
• Machine learning can’t solve an ethical problem.
• It cannot solve problems having ”Ambiguity” or “Variability” in their context:
. “Ambiguity” - this means that the same word can mean many things.
 “Variability” - indicates the same thing can be said in many different ways.
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