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.
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