CBSE Textbook: Modelling | Artificial Intelligence (AI) for Class 9 PDF Download

 Page 1


47 
 
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. 
1.2.4 Modelling 
 
Title: Modelling Approach: Session + Activity 
Summary: In this module, students’ progress from data exploration to AI modeling, learning 
about key distinctions between Artificial Intelligence (AI), Machine Learning (ML), and Deep 
Learning (DL). The module introduces two approaches to AI modeling: Rule-Based and 
Learning-Based. 
Learning Objectives: 
? Understand and differentiate between AI, ML, and DL. 
? Explain the differences between Rule-Based and Learning-Based AI approaches. 
? Develop a basic understanding of how AI models are trained and tested. 
Learning Outcomes: 
? Define AI, ML, and DL and explain their relationships. 
? Identify the key differences between Rule-Based and Learning-Based AI models. 
Pre-requisites: Basic understanding of AI concepts from previous modules. 
Key-concepts: 
? AI, ML and DL 
? Rule-Based Approach 
? Learning-Based Approach 
? AI Modeling 
In the previous module of Data Exploration, you explored the data you had acquired at the Data 
Acquisition stage for the problem you scoped in the Problem Scoping stage. Now, you have visualised 
some trends and patterns out of the data which would help you develop a strategy for your project. 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). 
AI, ML & DL 
Page 2


47 
 
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. 
1.2.4 Modelling 
 
Title: Modelling Approach: Session + Activity 
Summary: In this module, students’ progress from data exploration to AI modeling, learning 
about key distinctions between Artificial Intelligence (AI), Machine Learning (ML), and Deep 
Learning (DL). The module introduces two approaches to AI modeling: Rule-Based and 
Learning-Based. 
Learning Objectives: 
? Understand and differentiate between AI, ML, and DL. 
? Explain the differences between Rule-Based and Learning-Based AI approaches. 
? Develop a basic understanding of how AI models are trained and tested. 
Learning Outcomes: 
? Define AI, ML, and DL and explain their relationships. 
? Identify the key differences between Rule-Based and Learning-Based AI models. 
Pre-requisites: Basic understanding of AI concepts from previous modules. 
Key-concepts: 
? AI, ML and DL 
? Rule-Based Approach 
? Learning-Based Approach 
? AI Modeling 
In the previous module of Data Exploration, you explored the data you had acquired at the Data 
Acquisition stage for the problem you scoped in the Problem Scoping stage. Now, you have visualised 
some trends and patterns out of the data which would help you develop a strategy for your project. 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). 
AI, ML & DL 
48 
 
 
As you have been progressing towards building AI readiness, you must have come across a very common 
dilemma between AI and ML. Many of the times, these terms are used interchangeably but are they the same? 
Is there no difference between Machine Learning and Artificial Intelligence? Is Deep Learning also Artificial 
Intelligence? What exactly is Deep Learning? Let us see … 
 
 
As you can see in the Venn Diagram, 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. 
 
 
 
 
Defining the terms: 
1. Artificial Intelligence, or AI, refers to any technique that enables computers to mimic human 
intelligence. The AI-enabled machines think algorithmically and execute what they have been 
asked for intelligently. 
2. 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. 
3. 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. Then comes 
Machine Learning which is intermediately intelligent and Artificial Intelligence covers all the concepts 
and algorithms which, in some way or the other mimic human intelligence. 
 
Modelling 
Purpose: Classification of Models into Rule-based approach and Learning approach. 
Say: “In general, there are two approaches taken by researchers when building AI models. They either 
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 3


47 
 
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. 
1.2.4 Modelling 
 
Title: Modelling Approach: Session + Activity 
Summary: In this module, students’ progress from data exploration to AI modeling, learning 
about key distinctions between Artificial Intelligence (AI), Machine Learning (ML), and Deep 
Learning (DL). The module introduces two approaches to AI modeling: Rule-Based and 
Learning-Based. 
Learning Objectives: 
? Understand and differentiate between AI, ML, and DL. 
? Explain the differences between Rule-Based and Learning-Based AI approaches. 
? Develop a basic understanding of how AI models are trained and tested. 
Learning Outcomes: 
? Define AI, ML, and DL and explain their relationships. 
? Identify the key differences between Rule-Based and Learning-Based AI models. 
Pre-requisites: Basic understanding of AI concepts from previous modules. 
Key-concepts: 
? AI, ML and DL 
? Rule-Based Approach 
? Learning-Based Approach 
? AI Modeling 
In the previous module of Data Exploration, you explored the data you had acquired at the Data 
Acquisition stage for the problem you scoped in the Problem Scoping stage. Now, you have visualised 
some trends and patterns out of the data which would help you develop a strategy for your project. 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). 
AI, ML & DL 
48 
 
 
As you have been progressing towards building AI readiness, you must have come across a very common 
dilemma between AI and ML. Many of the times, these terms are used interchangeably but are they the same? 
Is there no difference between Machine Learning and Artificial Intelligence? Is Deep Learning also Artificial 
Intelligence? What exactly is Deep Learning? Let us see … 
 
 
As you can see in the Venn Diagram, 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. 
 
 
 
 
Defining the terms: 
1. Artificial Intelligence, or AI, refers to any technique that enables computers to mimic human 
intelligence. The AI-enabled machines think algorithmically and execute what they have been 
asked for intelligently. 
2. 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. 
3. 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. Then comes 
Machine Learning which is intermediately intelligent and Artificial Intelligence covers all the concepts 
and algorithms which, in some way or the other mimic human intelligence. 
 
Modelling 
Purpose: Classification of Models into Rule-based approach and Learning approach. 
Say: “In general, there are two approaches taken by researchers when building AI models. They either 
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.” 
49 
 
take a rule-based approach or learning approach. A Rule based approach is generally based on the data 
and rules fed to the machine, where the machine reacts accordingly to deliver the desired output. Under 
learning approach, the machine is fed with data and the desired output to which the machine designs its 
own algorithm (or set of rules) to match the data to the desired output fed into the machine” 
 
 
AI Modelling refers to developing algorithms, also called models which can be trained to get intelligent 
outputs. That is, writing codes to make a machine artificially intelligent. 
Let us ponder 
Use your knowledge and thinking ability and answer the following questions: 
1. What makes a machine intelligent? 
 
 
 
 
 
 
 
2. How can a machine be Artificially Intelligent? 
 
 
 
 
 
 
 
3. Can Artificial Intelligence be a threat to Human Intelligence? How? 
 
 
 
 
 
 
 
Page 4


47 
 
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. 
1.2.4 Modelling 
 
Title: Modelling Approach: Session + Activity 
Summary: In this module, students’ progress from data exploration to AI modeling, learning 
about key distinctions between Artificial Intelligence (AI), Machine Learning (ML), and Deep 
Learning (DL). The module introduces two approaches to AI modeling: Rule-Based and 
Learning-Based. 
Learning Objectives: 
? Understand and differentiate between AI, ML, and DL. 
? Explain the differences between Rule-Based and Learning-Based AI approaches. 
? Develop a basic understanding of how AI models are trained and tested. 
Learning Outcomes: 
? Define AI, ML, and DL and explain their relationships. 
? Identify the key differences between Rule-Based and Learning-Based AI models. 
Pre-requisites: Basic understanding of AI concepts from previous modules. 
Key-concepts: 
? AI, ML and DL 
? Rule-Based Approach 
? Learning-Based Approach 
? AI Modeling 
In the previous module of Data Exploration, you explored the data you had acquired at the Data 
Acquisition stage for the problem you scoped in the Problem Scoping stage. Now, you have visualised 
some trends and patterns out of the data which would help you develop a strategy for your project. 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). 
AI, ML & DL 
48 
 
 
As you have been progressing towards building AI readiness, you must have come across a very common 
dilemma between AI and ML. Many of the times, these terms are used interchangeably but are they the same? 
Is there no difference between Machine Learning and Artificial Intelligence? Is Deep Learning also Artificial 
Intelligence? What exactly is Deep Learning? Let us see … 
 
 
As you can see in the Venn Diagram, 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. 
 
 
 
 
Defining the terms: 
1. Artificial Intelligence, or AI, refers to any technique that enables computers to mimic human 
intelligence. The AI-enabled machines think algorithmically and execute what they have been 
asked for intelligently. 
2. 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. 
3. 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. Then comes 
Machine Learning which is intermediately intelligent and Artificial Intelligence covers all the concepts 
and algorithms which, in some way or the other mimic human intelligence. 
 
Modelling 
Purpose: Classification of Models into Rule-based approach and Learning approach. 
Say: “In general, there are two approaches taken by researchers when building AI models. They either 
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.” 
49 
 
take a rule-based approach or learning approach. A Rule based approach is generally based on the data 
and rules fed to the machine, where the machine reacts accordingly to deliver the desired output. Under 
learning approach, the machine is fed with data and the desired output to which the machine designs its 
own algorithm (or set of rules) to match the data to the desired output fed into the machine” 
 
 
AI Modelling refers to developing algorithms, also called models which can be trained to get intelligent 
outputs. That is, writing codes to make a machine artificially intelligent. 
Let us ponder 
Use your knowledge and thinking ability and answer the following questions: 
1. What makes a machine intelligent? 
 
 
 
 
 
 
 
2. How can a machine be Artificially Intelligent? 
 
 
 
 
 
 
 
3. Can Artificial Intelligence be a threat to Human Intelligence? How? 
 
 
 
 
 
 
 
50 
 
In the previous module of Data exploration, we have seen various types of graphical representations 
which can be used for representing different parameters of data. The graphical representation makes 
the data understandable for humans as we can discover trends and patterns out of it. But when it comes 
to machine accessing and analysing data, it needs the data in the most basic form of numbers (which is 
binary – 0s and 1s) and when it comes to discovering patterns and trends in data, the machine goes for 
mathematical representations of the same. The ability to mathematically describe the relationship 
between parameters is the heart of every AI model. Thus, whenever we talk about developing AI 
models, it is the mathematical approach towards analysing data which we refer to. 
 
Generally, AI models can be classified as follows: 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Rule Based Approach 
Refers to the Al modelling where the rules are defined by the developer. The machine follows the rules 
or instructions mentioned by the developer and performs its task accordingly. For example, we have a 
dataset which tells us about the conditions on the basis of which we can decide if child can go out to 
play golf or not. The parameters are: Outlook, Temperature, Humidity and Wind. Now, let's take various 
possibilities of these parameters and see in which case the children may play golf and in which case they 
cannot. After looking through all the cases, we feed this data into the machine along with the rules 
which tell the machine all the possibilities. The machine trains on this data and now is ready to be 
tested. While testing the machine, we tell the machine that Outlook Overcast; Temperature = Normal; 
Humidity = Normal and Wind = Weak. On the basis of this testing dataset, now the machine will be able 
to tell if the child can go out to play golf or not and will display the prediction to us. This is known as a 
rule-based approach because we fed the data along with rules to the machine and the machine after 
getting trained on them is now able to predict answers for the same. A drawback/feature for this 
approach is that the learning is static. The machine once trained, does not take into consideration any 
changes made in the original training dataset. 
Page 5


47 
 
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. 
1.2.4 Modelling 
 
Title: Modelling Approach: Session + Activity 
Summary: In this module, students’ progress from data exploration to AI modeling, learning 
about key distinctions between Artificial Intelligence (AI), Machine Learning (ML), and Deep 
Learning (DL). The module introduces two approaches to AI modeling: Rule-Based and 
Learning-Based. 
Learning Objectives: 
? Understand and differentiate between AI, ML, and DL. 
? Explain the differences between Rule-Based and Learning-Based AI approaches. 
? Develop a basic understanding of how AI models are trained and tested. 
Learning Outcomes: 
? Define AI, ML, and DL and explain their relationships. 
? Identify the key differences between Rule-Based and Learning-Based AI models. 
Pre-requisites: Basic understanding of AI concepts from previous modules. 
Key-concepts: 
? AI, ML and DL 
? Rule-Based Approach 
? Learning-Based Approach 
? AI Modeling 
In the previous module of Data Exploration, you explored the data you had acquired at the Data 
Acquisition stage for the problem you scoped in the Problem Scoping stage. Now, you have visualised 
some trends and patterns out of the data which would help you develop a strategy for your project. 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). 
AI, ML & DL 
48 
 
 
As you have been progressing towards building AI readiness, you must have come across a very common 
dilemma between AI and ML. Many of the times, these terms are used interchangeably but are they the same? 
Is there no difference between Machine Learning and Artificial Intelligence? Is Deep Learning also Artificial 
Intelligence? What exactly is Deep Learning? Let us see … 
 
 
As you can see in the Venn Diagram, 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. 
 
 
 
 
Defining the terms: 
1. Artificial Intelligence, or AI, refers to any technique that enables computers to mimic human 
intelligence. The AI-enabled machines think algorithmically and execute what they have been 
asked for intelligently. 
2. 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. 
3. 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. Then comes 
Machine Learning which is intermediately intelligent and Artificial Intelligence covers all the concepts 
and algorithms which, in some way or the other mimic human intelligence. 
 
Modelling 
Purpose: Classification of Models into Rule-based approach and Learning approach. 
Say: “In general, there are two approaches taken by researchers when building AI models. They either 
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.” 
49 
 
take a rule-based approach or learning approach. A Rule based approach is generally based on the data 
and rules fed to the machine, where the machine reacts accordingly to deliver the desired output. Under 
learning approach, the machine is fed with data and the desired output to which the machine designs its 
own algorithm (or set of rules) to match the data to the desired output fed into the machine” 
 
 
AI Modelling refers to developing algorithms, also called models which can be trained to get intelligent 
outputs. That is, writing codes to make a machine artificially intelligent. 
Let us ponder 
Use your knowledge and thinking ability and answer the following questions: 
1. What makes a machine intelligent? 
 
 
 
 
 
 
 
2. How can a machine be Artificially Intelligent? 
 
 
 
 
 
 
 
3. Can Artificial Intelligence be a threat to Human Intelligence? How? 
 
 
 
 
 
 
 
50 
 
In the previous module of Data exploration, we have seen various types of graphical representations 
which can be used for representing different parameters of data. The graphical representation makes 
the data understandable for humans as we can discover trends and patterns out of it. But when it comes 
to machine accessing and analysing data, it needs the data in the most basic form of numbers (which is 
binary – 0s and 1s) and when it comes to discovering patterns and trends in data, the machine goes for 
mathematical representations of the same. The ability to mathematically describe the relationship 
between parameters is the heart of every AI model. Thus, whenever we talk about developing AI 
models, it is the mathematical approach towards analysing data which we refer to. 
 
Generally, AI models can be classified as follows: 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Rule Based Approach 
Refers to the Al modelling where the rules are defined by the developer. The machine follows the rules 
or instructions mentioned by the developer and performs its task accordingly. For example, we have a 
dataset which tells us about the conditions on the basis of which we can decide if child can go out to 
play golf or not. The parameters are: Outlook, Temperature, Humidity and Wind. Now, let's take various 
possibilities of these parameters and see in which case the children may play golf and in which case they 
cannot. After looking through all the cases, we feed this data into the machine along with the rules 
which tell the machine all the possibilities. The machine trains on this data and now is ready to be 
tested. While testing the machine, we tell the machine that Outlook Overcast; Temperature = Normal; 
Humidity = Normal and Wind = Weak. On the basis of this testing dataset, now the machine will be able 
to tell if the child can go out to play golf or not and will display the prediction to us. This is known as a 
rule-based approach because we fed the data along with rules to the machine and the machine after 
getting trained on them is now able to predict answers for the same. A drawback/feature for this 
approach is that the learning is static. The machine once trained, does not take into consideration any 
changes made in the original training dataset. 
51 
 
Rule Based AI Model