Unit 2: Advanced Concept of Modelling Class 10 Notes

Unit 2: Advanced Concept of Modelling Class 10 Notes

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Artificial Intelligence is one of the most important topics in the CBSE Class 10 AI syllabus. In this chapter Advanced Concept of Modelling, students learn about AI models, Machine Learning, Deep Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, ANN, CNN, and Perceptrons. These notes provide easy explanations and examples for quick revision.

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Revisiting AI, ML, DL

Artificial Intelligence(AI): Artificial Intelligence refers to any technique that enables computers to mimic human intelligence. An artificially intelligent machine works on algorithms and data fed to it and gives the desired output.

Machine Learning (ML): Machine Learning enables machines to improve at tasks with experience. The machine here learns from the new data fed to it while testing and uses it for the next iteration.

Deep Learning (DL): Deep Learning enables software to train itself to perform tasks with vast amounts of data. In other words we can say that Deep learning enables the system to train itself with the help of multiple machine learning algorithms working altogether to perform a specific task.

NOTE: Artificial Intelligence is the umbrella term which holds both Deep Learning as well as Machine Learning.

Machine Learning (ML)

Machine Learning, or ML, enables machines to improve at tasks with experience. The machine learns from its mistakes and improves itself by learning from its own experiences.

Here is an example which shows labelled images (Apple or Strawberry) are given as input to the ML model. ML model learns from the input data to classify between apples and strawberries and predicts the correct output as shown.

Examples of Machine Learning (ML)

Object Classification

Identifies and labels objects present within an image or data point. It determines the category an object belongs to.

Anomaly Detection

Anomaly detection helps us to find the unexpected things which is hiding in our data. For example, tracking your heart rate, and finding a sudden spike could be an anomaly, flagging a potential issue.

More real-life examples of Anomaly Detection that are easy to understand:

1. Credit Card Fraud Detection

Normal	Anomaly
You buy coffee for ₹50 every morning	Suddenly, a purchase of ₹50,000 happens at 2 AM from another country

The system flags this as suspicious because it doesn’t match your normal spending pattern.

2. Student Attendance

Normal	Anomaly
A student attends 90% of classes	Suddenly, the student is absent for 15 days in a row

Anomaly detected → Could indicate illness, family problem, or dropping out.

Deep Learning (DL)

Deep Learning enables software to train itself to perform tasks with vast amounts of data. In other words we can say that Deep learning enables the system to train itself with the help of multiple machine learning algorithms working altogether to perform a specific task. Deep Learning is the most advanced form of Artificial Intelligence. Examples of Deep Learning (DL) are given below

Object Identification

Object identification enables a computer to identify and label the different objects which are in a picture. for example when we show a picture of kitchen to a computer it may identify and label objects like Fridge, Stove, Oven etc

Similarly when we show a picture of zoo to a computer it may identify and label objects like Tiger, Lion, Deer, Elephants etc.

Digit Recognition

Digit recognition in deep learning train computers to identify handwritten digits (0-9) within images.

Common terminologies used with data

What is Data?

Data are raw fact or figures that can be stored, processed, or analyzed. For example : the following table stores information about fruits is considered data.

Fruit	Colour	Weight	Price
Apple	Red	120g	Rs 45
Orange	Orange	100g	Rs 20
Banana	Yellow	80g	Rs 7

Each row will contain information about different fruits

Each column represents a feature (also called an attribute), such as colour, weight, or price

This table contain information about fruits is data

What do you mean by Features?

Features are characteristics used to describe data. In above table Colour, Weight and Price are features.

What are labels?

Labels are the output values or target values that we want to predict from the data. Data Labeling is the process of attaching meaning to data.

For e.g. if we are trying to predict what fruit it is based on the color of the fruit, then color is the feature, and fruit name is the label.

Data can be of two types – Labeled and Unlabeled

Labeled Data: Data to which some tag/label is attached. For e.g. Name, type, number, etc.

Unlabeled Data: Data to which not tag is attached is called unlabeled data. It is the raw form of data.

Labeled Data	Unlabeled Data
Contains tags	No tags
More organized	Raw form of data

What do you mean by a training data set?

The training data set is a collection of examples which is used to train the AI model. AI model uses training data set to learn patterns, make predictions, and improve its performance..

What do you mean by a testing data set?

A data set which is used to check the performance or accuracy of the AI model is called testing data set. It is usually different from the training data set.

Modelling

AI Modelling refers to the process of developing algorithms, also called models which can be trained to get intelligent outputs. In other words we can say that modelling means writing codes to make a machine artificially intelligent.

Types of AI Models

Generally, AI models can be classified as follows:

1. Rule-Based AI Models

These AI models follows the rules or instructions given by the developer, and performs its task accordingly. Rule-based Chatbots are commonly used on websites to answer frequently asked questions (FAQs) or provide basic customer support. Example of Rule based AI model

Scenario: A clothing website has a Chabot to answer questions about order tracking

1. Data: This chatbot uses:

• Predefined questions
• Predefined answers

Example:

• “Where is my order”
• “Track my order”
• “Where is my shipment?”
• “Where is my cashback?”

2. Rules: These chatbots uses pre defined rules like

1. Rule 1: If the user message contains keywords such as “track order”, “shipment status” or “delivery” then the chatbot moves to order tracking options.
2. Rule 2: Under order tracking options:
   • If the user asks for their order number, prompt them to enter it.
   • If the user enters a valid order number, retrieve the tracking information and display.
   • If the user enters an invalid order number, display an error message and prompt them to re-enter it.
3. Rule 3: If the user’s message doesn’t match any defined rules, then the chatbot will display a message like “Sorry, I can’t help. Contact customer care”

3. Interaction: When a user chats with the bot, the Chabot responds with a pre-written answer.

A drawback/feature for this approach is that the learning is static. That is, if you try testing the machine on a dataset which is different from the rules and data you fed it at the training stage, the machine will fail and will not learn from its mistake.

Thus, machine learning gets introduced as an extension to this as in that case, the machine adapts to change in data and rules and follows the updated path only, while a rule-based model does what it has been taught once.

2. Learning-Based AI Models

A learning-based approach is a method where a computer learns from examples not by rules. The computer learns to perform by analyzing data and finding patterns or rules on its own.

For example, suppose you have a dataset of 1000 images of random stray dogs. When we put this dataset into a learning approach-based AI machine, it might group the data on the basis of colour, size, fur style, etc.

We can say that In Learning Based approach, the AI model gets trained on the data fed to it and then is able to design a model which is adaptive to the change in data.

For example, A learning-based spam email filter is a computer program that automatically identifies whether an incoming email is spam or not.

During training, the filter is provided with a large dataset of emails. The filter analyzes the characteristics of these emails, such as words used, sender information, and presence of attachments. Using machine learning algorithms, the filter learns to distinguish spam from legitimate emails.

This Learning Based Approach improves its accuracy over time as it encounters new examples.

Categories of Machine learning based models

Machine learning can further be divided into three parts:

1. Supervised Learning:

In a supervised learning model, the dataset which is fed to the machine is labelled. In other words, we can say that the dataset is known to the person who is training the machine.

A label is some information which can be used as a tag for data. For example, students get grades according to the marks they secure in examinations. These grades are labels.

Supervised Learning is when you make the machine learn by teaching, just like a math teacher teaches the class by using a lot of solved examples(training) then test the knowledge of students by giving them some new problems.

In supervised learning, the machine learns from labeled data set and then applies that learning to new, unlabeled data set.

Supervised Learning – Example

Problem Statement: Build a model to predict the coin based on its weight.

Dataset (Labeled examples): We have the following dataset that we fed into the model.
▪ 1 Euro weighs 5 grams
▪ 1 Dirham weighs 7 grams
▪ 1 Dollar weighs 3 grams
▪ 1 Rupee weighs 4 grams

Feature (Input): Weight of the coin (in grams)

Label (Output): Currency name

2. Unsupervised Learning:

An unsupervised learning model works on unlabeled dataset. This means that the data which is fed to the machine is random. The unsupervised learning models are used to identify relationships, patterns and trends out of the data which is fed into it.

Unsupervised Learning is a type of learning without any guidance. For e.g. A child learning to swim on his own without any supervision. Here, the machine is responsible to discover patterns, similarities, and differences on its own based on the unlabeled dataset.

Unsupervised Learning – Example: An example of a supermarket

We have a customer database which shows the products bought by them. Now marketing manager of super market decides to send a grocery offer message to those customers who buys grocery regularly.

In the database, no customer is labeled as grocery shoppers and non-grocery shopper. So we feed the data into the unsupervised learning model and the model could discover patterns on its own and could come up with these two clusters/groups.

1. Grocery Shopper
2. Non Grocery Shopper

Test Yourself:

Identify the model: Supervised or Unsupervised?

Case 1: Social Media platforms: Identify your friend in a picture from an album of tagged photographs

Ans. It is a supervised learning

Case 2: OTT platform Recommendations based on someone’s watch history.

Ans. It is unsupervised learning

Case 3: Analyze bank data for suspicious-looking transactions and flag the fraud transactions.

Ans. It is unsupervised learning

Supervised Vs. Unsupervised Learning

Supervised Learning	Unsupervised Learning
It deals with labeled data.	It deals with Unlabeled data.
It helps to Predict the output for new data.	It helps to find the hidden patterns.
Less computing power is required as clean labeled data is used as input.	More computing power is required as unsorted and messy data is used as input.
for example: Student learning with a teacher & solved examples	for example: Student learning swimming on its own

3. Reinforcement Learning:

Reinforcement learning is a type of learning in which a machine learns to perform a task through a repeated trial-and-error method.

Let’s say you provide an image of an apple to the machine and ask the machine to predict it. The machine first predicts it as ‘cherry’ and you give negative feedback that it’s incorrect. Now, the machine learns that it’s not a cherry. Then again, you ask the machine to predict the fruit by giving an image of an apple as input. Now, the machine knows that it is not a cherry. It predicts it as an apple and you give positive feedback that it’s correct.

So, now the machine learns that this is an apple.

Real-life Examples of Reinforcement Learning

Self-driving car

Robot learning to walk

Summary of ML Models

• Supervised learning models are used when we want to determine relationships through training.
• Unsupervised learning models are used when we want to discover new patterns from data.
• Reinforcement learning models are used when we want to implement machine learning through a reward mechanism.

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Unit 2: Advanced Concept of Modelling Class 10 Notes

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Sub-categories of Supervised Learning Model

There are two types of Supervised Learning models: Classification model and Regression model.

1. Classification Model

Here the data is classified according to the labels. For example, in the grading system, students are classified on the basis of the grades they obtain

Examples of the Classification Model

a. Classify “hot” or “cold” weather tomorrow:

In this case, the model would be trained on historical weather data that labeled as “hot” or “cold”. The model would learn to differentiate hot and cold weather based on factors like:

• Location (average temperatures vary geographically)
• Season (summer vs. winter)
• High and low temperatures
• Humidity

b. Classifying emails as spam or not:

It decides whether an incoming email is “spam” (junk/unwanted) or “not spam” (safe/important).

2. Regression Model

These models work on continuous data. For example, if you wish to predict your next salary, then you would put in the data of your previous salary, any increments, etc., and would train the model.

Examples of the Regression Model

Example 1: Predicting temperature

Example 2: Predicting the price of the house

Example 3: Used Car Price Prediction

Test Yourself:

Identify the model: Classification or Regression?

Case 1: Predicting whether a customer is eligible for a bank loan or not?

It is Classification, since the model is going to predict whether or not the customer is eligible for a loan- The output will be either yes or no (discrete values)

Case 2: Predicting weather for next 24 hours

It is Regression because predicting weather for next 24 hours will be a continuous range (it will keep changing over the period of 24 hours)

Quick Check (for students)

Which one is Regression or Classification?

1. Predicting “rainy” or “sunny” → Classification (category)

2. Predicting “25°C” → Regression (number)

3. Predicting “spam” or “not spam” → Classification

4. Predicting “₹50,000 salary” → Regression

5. Predicting “pass” or “fail” → Classification

6. Predicting “87 marks in exam” → Regression

Sub-categories of Unsupervised Learning Model

Unsupervised learning models can be further divided into two categories: Clustering model and Association model.

What is Clustering?

Clustering is a process of dividing the data points into different groups or clusters based on their similarity between them.

In above example, we have input data with no labels the unsupervised learning model was able to divide this data into two clusters based on clustering.

The two clusters have been formed based on the similarity of characteristics. The first cluster comprises all the animals, and the second cluster comprises all the birds.

Difference between Clustering and Classification

Clustering	Classification
It is a supervised learning	It is an unsupervised learning
It uses labeled data	It uses unlabeled data
It uses predefined classes	It finds similarities between objects and places them in the same cluster

Association

Association Rule is an unsupervised learning method that is used to find interesting relationships between variables from the database. Consider a supermarket example wherein

• Customer A buys bread, butter, and milk

• Customer B buys rice, bread, and butter

Now can you predict any Customer X who buys bread will most probably buy?

Most probably Customer X will buy butter

Such meaningful associations can be useful to recommend items to customers. This is called Association Rule

Summary of detailed classification of ML models

Unit 2: Advanced Concept of Modelling Class 10 Notes

Test Yourself: Let’s have knowledge check based on learning approaches and its types!

Q1. Which learning approach uses labelled data for training?

1. Supervised Learning
2. UnSupervised Learning
3. Reinforcement Learning

Q2. The target variable is categorical in _____________________ Problem?

1. Regression
2. Clustering
3. Classification

Q3. Which algorithmic model would you use when you have to predict a continuous valued output?

1. Regression
2. Clustering
3. Classification

Q4. Which of the following is false about Reinforcement Learning?

1. Uses Reward Mechanism
2. Target is to Maximize the Reward
3. Predicts a continuous value as output

Q5. Clustering is learning and its goal is to ?

1. Supervised, Classify data points into different classes
2. Unsupervised, Divide the data points into different groups
3. Unsupervised, Predict the output based on input data points

Sub-Categories of Deep Learning

In deep learning, the machine is trained with huge amounts of data which helps it to train itself around the data. Such machines are intelligent enough to develop algorithms for themselves. There are two types of Deep Learning models: Artificial Neural Networks (ANN) and Convolution Neural Network (CNN).

Artificial Neural networks (ANN) – Artificial Neural networks are inspired by the human brain and nervous system. Every neural network node is essentially a machine learning algorithm. It is useful when solving problems for which the data set is very large.

Structure of a Neural Network/How Neural Network works?

A Neural Network is made up of different layers and nodes.

1. Input Layer: The first layer of a Neural Network is known as the input layer. The job of an input layer is to acquire data and feed it to the Neural Network. No processing occurs at the input layer.

2. Hidden Layer: These are the layers in which the whole processing occurs. These layers are not visible to the user. This layer performs computation by means of weights and biases Information. The process of finding the right output begins with trial and error until the network finally learns.

There can be multiple hidden layers in a neural network system and their number depends upon the complexity of the function for which the network has been configured. Also, the number of nodes in each layer can vary accordingly

3. Output Layer: It is the last layer of Neural Network. It receives the output from the last hidden layer. This layer also does not process the data. It is meant for user-interface.

Real-world applications of neural network are

1. Facial recognition
2. Customer support chatbot
3. Vegetable price prediction etc.

Convolutional Neural Network (CNN) – Convolutional Neural Network is a Deep Learning algorithm which can take in an input image, automatically learn important features such as edges, shapes, and objects in the image and be able to differentiate one from the other.

Unit 2: Advanced Concept of Modelling Class 10 Notes

How does AI make a Decision?

Let we understand this with the help of the following example

Suppose you planned to go out to the park today then the following things may come to your mind.

1. Do I have a jacket?
2. Do I have an umbrella?
3. Is it raining now?
4. What is the weather forecast for later?

All the above factors which may influence your decision are not equally important.

Now let us convert this to perceptron.

A perceptron is one of the simplest models of a Neural Network. It takes: Inputs Weights Bias Threshold and then gives an output or decision.

Let’s draw the perceptron with four inputs (from X1 to X4). Next, we have their weights (from W1 to W3). Then, we also have the bias B, with weight WB.
Finally, we sum them all up, compare with threshold, and we will get our output.

The four inputs are:

1. X1 = Do I have a jacket?
2. X2 = Do I have an umbrella?
3. X3 = Is it sunny now?
4. X4 = What is the weather forecast later?

Each input is given a value: Yes = 1 and No = 0

Weights show how important each factor is.

Example:

• Sunny weather may be more important
• Jacket may be more useful than umbrella

The weights are decided based on:

• Experience
• Personal preference
• Situation

Bias helps in making the final decision.

• A higher bias means the person is more cautious.
• A lower bias means the person is more daring.

In this example, bias value is taken as 4 as we want to be more cautious

Now we will fill in the importance. But the importance is not very useful in mathematical calculations. Next, let us assign some numbers to them. At the same time, we will also assign a number for the bias weight (WB).

There is no right or wrong answers in coming up with the values for the weights as the values are assigned by Personal Preference and Experiences.

That is the reason why everyone comes up with a different decision, even though the situation is the same for everyone.

Let’s consider: I have a jacket, I don’t have an umbrella, it is sunny now, and the weather forecast is going to rain. We can convert the yes and no to numbers 1 and For bias, we will always take 1. From this calculation, the output is 0.5. Since this is higher than the threshold (which is zero), the result is I will go out to the park.

Now let us change the value 1 to 0 and 0 to 1 for another example. From this calculation, the output is -0.5. Since this is lower than the threshold (which is zero), the result is I will not go out to the park.

Unit 2: Advanced Concept of Modelling Class 10 Notes

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Disclaimer : I tried to give you the easy handouts of “Unit 2: Advanced Concept of Modelling Class 10“, but if you feel that there is/are mistakes in the handouts of “Unit 2: Advanced Concept of Modelling Class 10“given above, you can directly contact me at csiplearninghub@gmail.com. NCERT Book and Study material available on CBSE official website are used as a reference to create above “Unit 2: Advanced Concept of Modelling Class 10“All the screenshots used in above article are taken from NCERT Book and Study material available on CBSE official website.

This Unit 2 Advanced Concept of Modelling Class 10 Notes cover Machine Learning, Deep Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, ANN, CNN, and Perceptron. Students can use these notes for quick revision and exam preparation.

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Unit 2: Advanced Concept of Modelling Class 10 Notes

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Important links of Class X (Artificial Intelligence)

Chapter 1 Introduction to AI MCQ

Chapter 1 Introduction to AI Class 10 NOTES

Chapter 2 AI Project Cycle MCQ

Chapter 3 Natural Language Processing MCQ

Important links of Class IX (IT-402)

Unit 1 : Introduction to IT–ITeS Industry BOOK SOLUTIONS

Unit 1 : Introduction to IT–ITeS Industry NOTES

Unit 1 : Introduction to IT-ITeS MCQ

Unit 3 : Digital Documentation NOTES

Unit 3 : Digital Documentation BOOK SOLUTIONS

Unit 3 : Digital Documentation MCQ

Unit 4 : Electronic Spreadsheet BOOK SOLUTIONS

Unit 4 : Electronic Spreadsheet MCQ

Unit 5 : Digital Presentation MCQ

Important links of Class X (IT – 402)

Unit 1: Digital Documentation (Advanced) using LibreOffice Writer

Chapter 1. Introduction to Styles – NOTES

Chapter 1. Introduction to Styles – Question Answers

Chapter 2. Working with Images – NOTES

Chapter 2. Working with Images – Question Answers

Chapter 3. Advanced features of Writer – NOTES

Chapter 3. Advanced features of Writer – Question Answers

Unit 2: Electronic Spreadsheet (Advanced) using LibreOffice Calc

Chapter 4. Analyse Data using Scenarios and Goal Seek – NOTES

Chapter 4. Analyse Data using Scenarios and Goal Seek – Question Answers

Chapter 5. Using Macros in Spreadsheet – NOTES

Chapter 5. Using Macros in Spreadsheet – Question Answers

Chapter 6. Linking Spreadsheet Data – NOTES

Chapter 6. Linking Spreadsheet Data – Question Answers

Chapter 7. Share and Review a Spreadsheet – NOTES

Chapter 7. Share and Review a Spreadsheet – Question Answers

Unit 3: Database Management system using LibreOffice Base

Chapter 8. Introduction to DBMS – NOTES

Chapter 8. Introduction to DBMS – Question Answers

Chapter 9. Starting with LibreOffice Base – NOTES

Chapter 9. Starting with LibreOffice Base – Question Answers

Chapter 10. Working with Multiples Tables – NOTES

Chapter 10. Working with Multiples Tables – Question Answers

Chapter 11. Queries in LibreOffice Base – NOTES

Chapter 11. Queries in LibreOffice Base – Question Answers

Chapter 12. Forms and Reports – NOTES

Chapter 12. Forms and Reports – Question Answers

Unit 4: Prevent Accident and Emergencies

Chapter 13. Health, Safety and Security at Workplace – NOTES

Chapter 13. Health, Safety and Security at Workplace – Question Answers

Chapter 14. Workplace Safety Measures – NOTES

Chapter 14. Workplace Safety Measures – Question Answers

Chapter 15. Prevent Accidents and Emergencies – NOTES

Chapter 15. Prevent Accidents and Emergencies – Question Answers

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Important links of Class X (IT – 402)

UNIT 1: DIGITAL DOCUMENTATION (ADVANCED) MCQ

UNIT-2: ELECTRONIC SPREADSHEET (ADVANCED) MCQ

UNIT-3 RELATIONAL DATABASE MANAGEMENT SYSTEMS (BASIC) MCQ

UNIT-4 WEB APPLICATIONS AND SECURITY MCQ

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Frequently Asked Questions (FAQs)

Q. What is AI Modelling?

AI Modelling is the process of creating algorithms that enable machines to perform intelligent tasks.

Q. What is the difference between Machine Learning and Deep Learning?

Machine Learning learns from data, while Deep Learning uses neural networks and large datasets to learn automatically.

Q. What is Supervised Learning?

In a supervised learning, the dataset which is fed to the machine is labelled.

Q. What is Reinforcement Learning?

Reinforcement learning is a type of learning in which a machine learns to perform a task through a repeated trial-and-error method.

Q. What is Unsupervised Learning?

In a unsupervised learning, the dataset which is fed to the machine is not labelled.

Q. Are these Class 10 AI Revision Notes enough for exams?
Yes, these notes cover all important concepts of Unit 2 Advanced Concepts of Modelling

Unit 2: Advanced Concept of Modelling Class 10 Notes

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