CBSE Textbook: Advanced Concepts of Modeling in AI | Artificial Intelligence for Class 10 PDF Download

 Page 1


                  
 
2.1 Revisiting AI, ML, DL 
 
To build an AI based project, we need to work around Artificially Intelligent models or 
algorithms. This could be done either by designing your own model or by using the pre-existing 
AI models. Before jumping into modelling let us clarify the definitions of Artificial Intelligence 
(AI), Machine Learning (ML) and Deep Learning (DL). 
 
 
Differentiate between AI, ML, and DL 
 
 
As you can see in the Venn Diagram given below, Artificial Intelligence is the umbrella 
terminology which covers machine and deep learning under it and Deep Learning comes 
under Machine Learning. It is a funnel type approach where there are a lot of applications of 
AI out of which few are those which come under ML out of which very few go into DL. 
Purpose: To differentiate between Artificial Intelligence (AI), Machine Learning (ML) and 
Deep Learning (DL). 
Say: “As we enter the world of modelling, it is a good time to clarify something many of you may be 
having doubts about. You may have heard the terms AI, ML and DL when research content online and 
during this course. They are of course related, but how? 
Artificial Intelligence, or AI for short, 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, or ML for short, 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. It also takes into account 
the times when it went wrong and considers the exceptions too. 
Deep Learning, or DL for short, enables software to train itself to perform tasks with vast amounts of data. 
Since the system has got huge set of data, it is able to train itself with the help of multiple machine 
learning algorithms working altogether to perform a specific task. 
Artificial Intelligence is the umbrella term which holds both Deep Learning as well as Machine Learning. 
Deep Learning, on the other hand, is the very specific learning approach which is a subset of 
Machine Learning as it comprises of multiple Machine Learning algorithms. ” 
Page 2


                  
 
2.1 Revisiting AI, ML, DL 
 
To build an AI based project, we need to work around Artificially Intelligent models or 
algorithms. This could be done either by designing your own model or by using the pre-existing 
AI models. Before jumping into modelling let us clarify the definitions of Artificial Intelligence 
(AI), Machine Learning (ML) and Deep Learning (DL). 
 
 
Differentiate between AI, ML, and DL 
 
 
As you can see in the Venn Diagram given below, Artificial Intelligence is the umbrella 
terminology which covers machine and deep learning under it and Deep Learning comes 
under Machine Learning. It is a funnel type approach where there are a lot of applications of 
AI out of which few are those which come under ML out of which very few go into DL. 
Purpose: To differentiate between Artificial Intelligence (AI), Machine Learning (ML) and 
Deep Learning (DL). 
Say: “As we enter the world of modelling, it is a good time to clarify something many of you may be 
having doubts about. You may have heard the terms AI, ML and DL when research content online and 
during this course. They are of course related, but how? 
Artificial Intelligence, or AI for short, 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, or ML for short, 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. It also takes into account 
the times when it went wrong and considers the exceptions too. 
Deep Learning, or DL for short, enables software to train itself to perform tasks with vast amounts of data. 
Since the system has got huge set of data, it is able to train itself with the help of multiple machine 
learning algorithms working altogether to perform a specific task. 
Artificial Intelligence is the umbrella term which holds both Deep Learning as well as Machine Learning. 
Deep Learning, on the other hand, is the very specific learning approach which is a subset of 
Machine Learning as it comprises of multiple Machine Learning algorithms. ” 
                  
 
 
 
 
 
Machine Learning (ML) 
Machine Learning, or ML, enables machines to improve at tasks with experience. The 
machine learns from its mistakes and takes them into consideration in the next execution. It 
improvises itself using its own experiences. 
 
 
 
 
Block Representation – Machine Learning (ML) 
 
This is just a broad representation of how a machine learning model works. Input (past or 
historical data) is given to the ML model and the model generates output by learning from the 
input data. 
Page 3


                  
 
2.1 Revisiting AI, ML, DL 
 
To build an AI based project, we need to work around Artificially Intelligent models or 
algorithms. This could be done either by designing your own model or by using the pre-existing 
AI models. Before jumping into modelling let us clarify the definitions of Artificial Intelligence 
(AI), Machine Learning (ML) and Deep Learning (DL). 
 
 
Differentiate between AI, ML, and DL 
 
 
As you can see in the Venn Diagram given below, Artificial Intelligence is the umbrella 
terminology which covers machine and deep learning under it and Deep Learning comes 
under Machine Learning. It is a funnel type approach where there are a lot of applications of 
AI out of which few are those which come under ML out of which very few go into DL. 
Purpose: To differentiate between Artificial Intelligence (AI), Machine Learning (ML) and 
Deep Learning (DL). 
Say: “As we enter the world of modelling, it is a good time to clarify something many of you may be 
having doubts about. You may have heard the terms AI, ML and DL when research content online and 
during this course. They are of course related, but how? 
Artificial Intelligence, or AI for short, 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, or ML for short, 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. It also takes into account 
the times when it went wrong and considers the exceptions too. 
Deep Learning, or DL for short, enables software to train itself to perform tasks with vast amounts of data. 
Since the system has got huge set of data, it is able to train itself with the help of multiple machine 
learning algorithms working altogether to perform a specific task. 
Artificial Intelligence is the umbrella term which holds both Deep Learning as well as Machine Learning. 
Deep Learning, on the other hand, is the very specific learning approach which is a subset of 
Machine Learning as it comprises of multiple Machine Learning algorithms. ” 
                  
 
 
 
 
 
Machine Learning (ML) 
Machine Learning, or ML, enables machines to improve at tasks with experience. The 
machine learns from its mistakes and takes them into consideration in the next execution. It 
improvises itself using its own experiences. 
 
 
 
 
Block Representation – Machine Learning (ML) 
 
This is just a broad representation of how a machine learning model works. Input (past or 
historical data) is given to the ML model and the model generates output by learning from the 
input data. 
                  
 
Here is an example which shows labelled images (every image is tagged either as 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 find the 
unexpected things hiding in our data. For 
example, tracking your heart rate, and finding 
a sudden spike could be an anomaly, flagging 
a potential issue. 
 
 
 
 
 
Deep Learning (DL) 
Deep Learning, or DL, enables software to train itself to perform tasks with vast amounts of 
data. In deep learning, the machine is trained with huge amounts of data which helps it into 
training itself around the data. Such machines are intelligent enough to develop algorithms for 
themselves. Deep Learning is the most advanced form of Artificial Intelligence out of these 
three. Following is the block diagram of deep learning: 
Page 4


                  
 
2.1 Revisiting AI, ML, DL 
 
To build an AI based project, we need to work around Artificially Intelligent models or 
algorithms. This could be done either by designing your own model or by using the pre-existing 
AI models. Before jumping into modelling let us clarify the definitions of Artificial Intelligence 
(AI), Machine Learning (ML) and Deep Learning (DL). 
 
 
Differentiate between AI, ML, and DL 
 
 
As you can see in the Venn Diagram given below, Artificial Intelligence is the umbrella 
terminology which covers machine and deep learning under it and Deep Learning comes 
under Machine Learning. It is a funnel type approach where there are a lot of applications of 
AI out of which few are those which come under ML out of which very few go into DL. 
Purpose: To differentiate between Artificial Intelligence (AI), Machine Learning (ML) and 
Deep Learning (DL). 
Say: “As we enter the world of modelling, it is a good time to clarify something many of you may be 
having doubts about. You may have heard the terms AI, ML and DL when research content online and 
during this course. They are of course related, but how? 
Artificial Intelligence, or AI for short, 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, or ML for short, 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. It also takes into account 
the times when it went wrong and considers the exceptions too. 
Deep Learning, or DL for short, enables software to train itself to perform tasks with vast amounts of data. 
Since the system has got huge set of data, it is able to train itself with the help of multiple machine 
learning algorithms working altogether to perform a specific task. 
Artificial Intelligence is the umbrella term which holds both Deep Learning as well as Machine Learning. 
Deep Learning, on the other hand, is the very specific learning approach which is a subset of 
Machine Learning as it comprises of multiple Machine Learning algorithms. ” 
                  
 
 
 
 
 
Machine Learning (ML) 
Machine Learning, or ML, enables machines to improve at tasks with experience. The 
machine learns from its mistakes and takes them into consideration in the next execution. It 
improvises itself using its own experiences. 
 
 
 
 
Block Representation – Machine Learning (ML) 
 
This is just a broad representation of how a machine learning model works. Input (past or 
historical data) is given to the ML model and the model generates output by learning from the 
input data. 
                  
 
Here is an example which shows labelled images (every image is tagged either as 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 find the 
unexpected things hiding in our data. For 
example, tracking your heart rate, and finding 
a sudden spike could be an anomaly, flagging 
a potential issue. 
 
 
 
 
 
Deep Learning (DL) 
Deep Learning, or DL, enables software to train itself to perform tasks with vast amounts of 
data. In deep learning, the machine is trained with huge amounts of data which helps it into 
training itself around the data. Such machines are intelligent enough to develop algorithms for 
themselves. Deep Learning is the most advanced form of Artificial Intelligence out of these 
three. Following is the block diagram of deep learning: 
                  
 
 
 
Block Representation- Deep Learning (DL) 
 
 
 
Input is given to an ANN, and after processing, the output is generated by the DL block. Here 
is an example which shows pixels of a bird image given as input to the DL Model and the model 
is able to analyze and correctly predict that it is a bird using a deep learning algorithm (ANN). 
 
Examples of Deep Learning (DL) 
Object Identification 
Object classification in deep learning 
tackles the task of identifying and labeling 
objects within an image. It essentially uses 
powerful algorithms to figure out what's 
in a picture and categorize those things. 
 
 
 
Digit Recognition 
Digit recognition in deep learning tackles 
the challenge of training computers to 
identify handwritten digits (0-9) within 
images.
Page 5


                  
 
2.1 Revisiting AI, ML, DL 
 
To build an AI based project, we need to work around Artificially Intelligent models or 
algorithms. This could be done either by designing your own model or by using the pre-existing 
AI models. Before jumping into modelling let us clarify the definitions of Artificial Intelligence 
(AI), Machine Learning (ML) and Deep Learning (DL). 
 
 
Differentiate between AI, ML, and DL 
 
 
As you can see in the Venn Diagram given below, Artificial Intelligence is the umbrella 
terminology which covers machine and deep learning under it and Deep Learning comes 
under Machine Learning. It is a funnel type approach where there are a lot of applications of 
AI out of which few are those which come under ML out of which very few go into DL. 
Purpose: To differentiate between Artificial Intelligence (AI), Machine Learning (ML) and 
Deep Learning (DL). 
Say: “As we enter the world of modelling, it is a good time to clarify something many of you may be 
having doubts about. You may have heard the terms AI, ML and DL when research content online and 
during this course. They are of course related, but how? 
Artificial Intelligence, or AI for short, 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, or ML for short, 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. It also takes into account 
the times when it went wrong and considers the exceptions too. 
Deep Learning, or DL for short, enables software to train itself to perform tasks with vast amounts of data. 
Since the system has got huge set of data, it is able to train itself with the help of multiple machine 
learning algorithms working altogether to perform a specific task. 
Artificial Intelligence is the umbrella term which holds both Deep Learning as well as Machine Learning. 
Deep Learning, on the other hand, is the very specific learning approach which is a subset of 
Machine Learning as it comprises of multiple Machine Learning algorithms. ” 
                  
 
 
 
 
 
Machine Learning (ML) 
Machine Learning, or ML, enables machines to improve at tasks with experience. The 
machine learns from its mistakes and takes them into consideration in the next execution. It 
improvises itself using its own experiences. 
 
 
 
 
Block Representation – Machine Learning (ML) 
 
This is just a broad representation of how a machine learning model works. Input (past or 
historical data) is given to the ML model and the model generates output by learning from the 
input data. 
                  
 
Here is an example which shows labelled images (every image is tagged either as 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 find the 
unexpected things hiding in our data. For 
example, tracking your heart rate, and finding 
a sudden spike could be an anomaly, flagging 
a potential issue. 
 
 
 
 
 
Deep Learning (DL) 
Deep Learning, or DL, enables software to train itself to perform tasks with vast amounts of 
data. In deep learning, the machine is trained with huge amounts of data which helps it into 
training itself around the data. Such machines are intelligent enough to develop algorithms for 
themselves. Deep Learning is the most advanced form of Artificial Intelligence out of these 
three. Following is the block diagram of deep learning: 
                  
 
 
 
Block Representation- Deep Learning (DL) 
 
 
 
Input is given to an ANN, and after processing, the output is generated by the DL block. Here 
is an example which shows pixels of a bird image given as input to the DL Model and the model 
is able to analyze and correctly predict that it is a bird using a deep learning algorithm (ANN). 
 
Examples of Deep Learning (DL) 
Object Identification 
Object classification in deep learning 
tackles the task of identifying and labeling 
objects within an image. It essentially uses 
powerful algorithms to figure out what's 
in a picture and categorize those things. 
 
 
 
Digit Recognition 
Digit recognition in deep learning tackles 
the challenge of training computers to 
identify handwritten digits (0-9) within 
images.
                  
 
Common terminologies used with data 
 
 
What is Data? 
? Data is information in any form 
? For e.g. A table with information about 
fruits is data 
? Each row will contain information about 
different fruits 
? Each fruit is described by certain features 
 
What do you mean by Features? 
• Columns of the tables are called features 
• In the fruit dataset example, features may be 
name, color, size, etc. 
• Some features are special, they are called 
labels 
 
What are Labels? 
Data Labeling is the process of attaching 
meaning to data 
• It depends on the context of the problem we are 
trying to solve 
• 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 
? The raw form of data 
? Data to which no tag is attached. 
 
What do you mean by a training data set? 
? The training data set is a collection of examples given to the model to analyze and 
learn. 
? Just like how a teacher teaches a topic to the class through a lot of examples 
and illustrations. 
? Similarly, a set of labeled data is used to train the AI model.