Machine learning is an application of artificial intelligence where a machine learns from past experiences (input data) and makes future predictions. Machine learning is an application of artificial intelligence where a machine learns from past experiences (or input data) and makes future predictions.

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Machine Learning
Manash Kumar Mondal
Department of Computer Science and Engineering
University of Kalyani
November, 2024
Manash Kumar Mondal Department of Computer Science and Engineering University of Kalyani
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1 AI Models
2 Machine Learning
3 Types of Machine Learning
4 Supervised Learning
5 Unsupervised Learning
6 Reinforcement Learning
7 Practical Case studies on AI.
Manash Kumar Mondal Department of Computer Science and Engineering University of Kalyani
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1 AI Models
2 Machine Learning
3 Types of Machine Learning
4 Supervised Learning
5 Unsupervised Learning
6 Reinforcement Learning
7 Practical Case studies on AI.
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What is Data Modeling ?
• Data modeling is the process of creating a visual
representation of an organization’s data, including the types
of data collected, how they relate to each other, and how they
will be stored and analyzed.
• The graphical representation makes the data understandable
for humans as we can discover trends and patterns from it,
but machine can analyze the data only when the data is in the
most basic form of numbers (which is binary 0s and 1s).
• The ability to mathematically describe the relationship
between parameters is the heart of every AI model.
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Ai Models
Generally, AI models can be classified as follows:
Figure 1: AI Models
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Rule Based Approach
• In this approach, the rules are defined by the developer. The
machine follows the rules or instructions mentioned by the
developer and performs its task accordingly.
• So it’s a static model that the machine wants to be trained
and does not take into consideration any changes made in the
original training data set.
Figure 2: Rule Based Approach
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Learning Based Approach
• It is a type of AI modeling where the machine learns by itself.
Under the learning-based approach, the AI model gets a train
on the data fed to it and then is able to design a model which
is adaptive to the change in data.
• If the model is training with X type of data and the machine
designs the algorithm around it, the model would modify itself
according to the changes in the data so that all the
expectations are handled in this case.
Figure 3: Learning Based Approach
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Conventional programming vs Machine learning
Conventional programming and Machine learning coding are both
computer programs, but their approach and objects are different.
• Conventional programming
refers to any manually
created program that uses
input data runs on a
computer and produces
output.
• In machine learning, the
input data, and the output
data are fed to an algorithm
(machine learning algorithm)
to create a program.
Figure 4: Conventional programming
Figure 5: Machine learning
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1 AI Models
2 Machine Learning
3 Types of Machine Learning
4 Supervised Learning
5 Unsupervised Learning
6 Reinforcement Learning
7 Practical Case studies on AI.
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What is Machine Learning?
• Machine learning (ML) is a
type of artificial intelligence
(AI) that allows computers
to learn and improve without
explicit programming.
• It uses mathematical models
to analyze data, identify
patterns, and make
predictions
Figure 6: Machine Learning
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Machine Learning
Machine learning is a subfield of
artificial intelligence (AI) that
uses algorithms trained on data
sets to create self-learning
models that are capable of
predicting outcomes and
classifying information without
human intervention.
Figure 7: Machine Learning
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How Machine Learning Works
• Data collection: Relevant data is collected from sources like
databases, sensors, or the internet.
• Data preprocessing: The data is preprocessed to ensure it’s
suitable for analysis.
• Model training: A machine learning model is trained to learn
how to make predictions or decisions from the input data.
• Feature selection: The model selects the most relevant
features from the data that will have the biggest impact on its
performance.
• Model evaluation: The model’s performance is assessed to
determine if it meets the desired criteria.
• Model deployment: The model is deployed in real-world
applications.
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How Machine Learning Works
Figure 8: How Machine Learning Works
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1 AI Models
2 Machine Learning
3 Types of Machine Learning
4 Supervised Learning
5 Unsupervised Learning
6 Reinforcement Learning
7 Practical Case studies on AI.
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Types of Machine Learning
Machine learning is divided into three categories:
• Supervised learning
• Unsupervised learning
• Reinforcement
learning
Figure 9: Types of Machine learning
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Labeled data and Unlabeled data
Figure 10: Labeled data and Unlabeled data
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1 AI Models
2 Machine Learning
3 Types of Machine Learning
4 Supervised Learning
5 Unsupervised Learning
6 Reinforcement Learning
7 Practical Case studies on AI.
Manash Kumar Mondal Department of Computer Science and Engineering University of Kalyani
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Supervised learning
• Supervised learning occurs
in the presence of a
supervisor or a teacher.
• We train the machine with
level data (i.e. some data is
already tagged with correct
answers), and it is then
compared to the learning.
• A supervised learning
algorithm learns from
labeled training data and
then becomes ready to
predict the outcomes for
unforeseen data.
Figure 11: Supervised learning
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How Supervised Learning Algorithms Work?
Figure 12: How Supervised Learning Algorithms Work?
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Supervised Learning Example: Classification
Figure 13: Classification
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Real life examples of Supervised learning
• Spam detection: Machine learning can be used to detect
spam in emails.
• Fraud detection: Machine learning models can be trained to
identify transactions that are likely fraudulent.
• Stock price prediction: Machine learning can be used to
predict stock prices.
• Medical diagnosis: Machine learning can be used to assist in
medical diagnosis.
• Traffic predictions: Machine learning can be used to predict
how long a trip will take based on historical traffic data.
• Sentiment analysis: Machine learning can be used to
determine the sentiment or opinion of a speaker or writer.
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1 AI Models
2 Machine Learning
3 Types of Machine Learning
4 Supervised Learning
5 Unsupervised Learning
6 Reinforcement Learning
7 Practical Case studies on AI.
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Unsupervised learning
Unsupervised learning is an ML technique where we don’t need to
supply labeled 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
resemblance, patterns, and
variance without any formal
training of data.
• In this kind of learning, the
machine is restricted to
finding a hidden structure in
the unlabeled data without
guidance or supervision.
Figure 14: Unsupervised learning
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How Unsupervised Learning Algorithms Work?
• Step 1: you provide the system with data that contains
photos of different kinds of animals and ask it to segregate
them. Remember, in the case of unsupervised learning, you
dont need to provide labeled data.
• Step 2: the system will look for patterns in the data. Patterns
like shape, colour, and size, and group the animals based on
those attributes.
Figure 15: How Unsupervised Learning Algorithms Work?
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Unsupervised Learning Example: Clustering
Figure 16: Clustering
Figure 17: Clustering result
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Real Life Examples of Unsupervised Learning
Here are some real-world examples of unsupervised learning:
• Recommendation engines Online retailers use unsupervised
learning to analyze transactional data and identify patterns
that can be used to create personalized recommendations.
Netflix uses unsupervised learning to analyze viewing habits to
create personalized suggestions.
• Customer segmentation Unsupervised learning can be used
to create buyer persona profiles by clustering customers based
on their common traits or purchasing behaviors.
• Fraud detection Unsupervised learning can be used to
identify unusual data points in datasets, such as fraudulent
transactions or bot activity.
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Real Life Examples of Unsupervised Learning
Here are some real-world examples of unsupervised learning:
• Natural language processing (NLP) Unsupervised learning
can be used for various NLP applications, such as categorizing
articles in news sections, text translation, and speech
recognition.
• Anomaly detection Unsupervised learning can be used to
identify data points, events, or observations that deviate from
a dataset’s normal behavior. For example, unsupervised
learning can be used to identify climate anomalies in satellite
images.
• Genetic research Genetic clustering is another common
example of unsupervised learning.
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1 AI Models
2 Machine Learning
3 Types of Machine Learning
4 Supervised Learning
5 Unsupervised Learning
6 Reinforcement Learning
7 Practical Case studies on AI.
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Reinforcement learning (RL)
Reinforcement learning (RL) is a
machine learning technique that teaches
software to make decisions that
maximize rewards in a dynamic
environment. It’s a powerful tool for
helping artificial intelligence (AI)
systems achieve optimal outcomes in
new environments. RL is used in
robotics and other decision-making
settings. For example, OpenAI and
DeepMind trained agents to play Atari
games based on human preferences
using RLHF (reinforcement learning
from human feedback).
Figure 18: Reinforcement
learning
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Reinforcement learning (RL)
Here are some key aspects of reinforcement learning:
• Trial-and-error learning: RL mimics how humans learn by
trial and error to achieve their goals.
• Reward-and-punishment: RL algorithms use a
reward-and-punishment paradigm to learn from the feedback
of each action.
• Delayed gratification: RL algorithms can consider the
delayed reward of an action, not just the immediate reward.
• Autonomous agents: RL agents learn to perform tasks
without guidance from a human user.
• Iterative process: Training an agent using RL is an iterative
process that can require returning to earlier stages in the
learning workflow.
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Real life examples of Reinforcement learning
Reinforcement learning (RL) is an artificial intelligence (AI)
technique that can be applied in many real-world scenarios,
including:
• Autonomous vehicles: RL helps cars navigate complex
environments, making self-driving technology safer and more
reliable.
• Natural language processing (NLP):RL can be used for
text summarization, question answering, and machine
translation. RL agents study language patterns to mimic and
predict how people speak.
• News recommendation:RL systems can track a reader’s
return behaviors to recommend news based on their
preferences.
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Real life examples of Reinforcement learning
• Trading and finance: RL can be used to predict future sales
and stock prices.
• Healthcare: RL systems can learn policies to treat patients.
• Robotics:RL-based robots can perform various tasks in
industry.
• Gaming: RL is used in gaming applications.
• Marketing and advertising: RL can be used in real-time
bidding.
RL is also used in engineering, where Facebook developed an
open-source RL platform called Horizon.
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Applications of Machine Learning
• Image Recognition
• Speech Recognition
• Traffic prediction
• Product recommendations
• Self-driving cars
• Email Spam and Malware
Filtering
• Virtual Personal Assistant
• Online Fraud Detection
• Stock Market trading
• Medical Diagnosis
• Automatic Language
Translation
Figure 19: Applications of Machine
Learning
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1 AI Models
2 Machine Learning
3 Types of Machine Learning
4 Supervised Learning
5 Unsupervised Learning
6 Reinforcement Learning
7 Practical Case studies on AI.
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Case Study 1: AI in Healthcare - Predictive Diagnostics
Example: IBM Watson Health for Cancer Treatment
How It Works: AI analyzes medical data to help doctors diagnose
diseases and suggest treatments.
Benefits:
• Faster, accurate diagnoses
• Personalized treatment plans
Activity: Discuss how AI could help with early diagnosis of
different diseases.
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Case Study 2: AI in Agriculture - Smart Farming
Example: Precision Agriculture with Drones and Sensors
How It Works: Drones and sensors use AI to monitor soil, crop
health, and water needs for better yield.
Benefits:
• Increases crop yield
• Reduces resource waste
Activity:Discuss how AI could improve crop production for a
specific type of crop in India
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Case Study 3: AI in Daily Life - Smart Assistants
Example: Virtual Assistants (Alexa, Siri)
How It Works: AI understands spoken language to perform tasks
like setting reminders, answering questions, and playing music.
Benefits:
• Convenient, hands-free assistance
• Helps in daily tasks
Activity: Try using a voice assistant and discuss how it
understands different languages.
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Conclusion
Data processing is an essential step in data science and machine
learning.
• Proper preprocessing leads to better model accuracy.
• The steps include data cleaning, integration, transformation,
and more.
Remember: Good data is the foundation of good insights.
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Thank You
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