[Audio] Ai Project Cycle By A V Amarnath. aI PROJECT. 

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[Audio] The (A-I ) Project Cycle is a structured process that guides the creation and deployment of (A-I ) solutions. It involves the following stages : 1 Problem Scoping: Identifying and understanding the problem that needs solving with (A-I ). This includes defining objectives and what success looks like. 2 Data Acquisition: Gathering data relevant to the problem, which can come from various sources like surveys, sensors, or existing datasets. 3 Data Exploration: Analysing the data to understand its structure, quality, and potential patterns. This is where we clean and organize the data to make it ready for (A-I ) models. 4 Modelling: Choosing and applying appropriate algorithms to build an (A-I ) model. This is where the (A-I ) learns from the data. 5 Evaluation: Testing the model to see how accurately it solves the problem, using metrics to measure its performance. 

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The AI Project Cycle is a structured process that
  guides the creation and deployment of AI solutions.
  It involves the following stages :

  1 Problem Scoping:   Identifying and understanding
  the problem that needs solving with AI. This
  includes defining objectives and what success looks
  like.
  2 Data Acquisition: Gathering data relevant to the
  problem, which can come from various sources like
  surveys, sensors, or existing datasets.

  3 Data Exploration: Analysing the data to 
 understand its structure, quality, and potential
  patterns. This is where we clean and organize the
  data to make it ready for AI models.

 4 Modelling: Choosing and applying appropriate 
 algorithms to build an AI model. This is where the AI
  learns from the data.

  5 Evaluation: Testing the model to see how
  accurately it solves the problem, using metrics to
  measure its performance

[Audio] Problem Scoping v v v v This is the first and the crucial stage of (A-I ) project development which focuses on identifying and understanding problems using 4Ws—Who, What, Where and Why. It is the analytics approach that involves taking steps to solve the problems and setting up goals that we want our project to achieve. Scoping a problem is not that easy as we need to have a deeper understanding around it so that we have clarity as to what we want our project to achieve while we are working to solve it. Hence, we use the A-W-S Problem Scoping Canvas to help us out.. 

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This is the first and the crucial stage of AI project development which focuses on identifying and understanding problems using 4Ws—Who, What, Where and Why.
 It is the analytics approach that involves taking steps to solve the problems and setting up goals that we want our project to achieve.
 Scoping a problem is not that easy as we need to have a deeper understanding around it so that we have clarity as to what we want our project to achieve while we are working to solve it. Hence, we use the AWS Problem Scoping Canvas to help us out.

[Audio] We are surrounded by problems. We are so used to these problems around us that they seem to be part of our lives. When we cannot observe a problem around us, then we should refer to the 17 goals that have been announced by the United Nations as the Sustainable Development Goals. These goals are to be achieved by 2030 as pledged by member nations of the UN. Artificial Intelligence supported solutions are suggested to assist society and government to achieve these goals that would work to improve the lives of the people living in poverty all across the nations. Sustainable Development Goals. 

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We are surrounded by problems. We are so used to these problems around us that they seem to be part of our lives. When we cannot observe a problem around us, then we should refer to the 17 goals that have been announced by the United Nations as the Sustainable Development Goals. These goals are to be achieved by 2030 as pledged by member nations of the UN. Artificial Intelligence supported solutions are suggested to assist society and government to achieve these goals that would work to improve the lives of the people living in poverty all across the nations.

THE SUSTAINABLE DEVELOPMENT GOALS
GOOD
CLEAN EOGY
DECENT WORX
WATER
MOUAUTES
PUCE JUSTICE
ANOSTRX
GENDER
CLEAN WAIER
CO
SUSTAINABLE
DEVELOPMENT
GOALS

[Audio] This is the second stage of the (A-I ) project cycle. It involves collecting data required for training the (A-I ) model. Data is raw information that is used to generate meaningful outcomes. For example, if we want to create an (A-I ) system to predict traffic flow for a specific geographic area, we would need previous traffic data. This data is essential for training the system. The training data allows the (A-I ) model to make predictions about future traffic patterns. The data from the previous year or past periods, which is used to feed into the system, is known as Training Data. The data used to test the accuracy of these predictions is known as Testing Data. The efficiency of the (A-I ) system depends on the authenticity and relevance of the training data. If the traffic data used is inaccurate, or if it's for a different location or time, then the AI’s predictions will not be reliable. To ensure effective performance, it is crucial to use relevant and accurate data, aligned with the problem statement. Data Acquisition. 

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This is the second stage of the AI project cycle. It involves collecting data required for training the AI model. Data is raw information that is used to generate meaningful outcomes.
 For example, if we want to create an AI system to predict traffic flow for a specific geographic area, we would need previous traffic data. This data is essential for training the system. The training data allows the AI model to make predictions about future traffic patterns. The data from the previous year or past periods, which is used to feed into the system, is known as Training Data. The data used to test the accuracy of these predictions is known as Testing Data.
 The efficiency of the AI system depends on the authenticity and relevance of the training data. If the traffic data used is inaccurate, or if it's for a different location or time, then the AI’s predictions will not be reliable.
 To ensure effective performance, it is crucial to use relevant and accurate data, aligned with the problem statement.

[Audio] Data Exploration This is the third stage in the Al project cycle. It refers to exploring the large data to uncover the patterns or trends needed for the Al project. It is considered to be the first step in data analysis where unstructured data is explored, researched, filtered and visualised to decide the strategy for the type of model used in the later stage.. 

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This is the third stage in the Al project cycle. It refers to exploring the large data to uncover the patterns or trends needed for the Al project.
 It is considered to be the first step in data analysis where unstructured data is explored, researched, filtered and visualised to decide the strategy for the type of model used in the later stage.

[Audio] An important stage in the process of Al project cycle where we decide on the technique to be followed for building a model from the prepared data. It is a mathematical approach in which an algorithm is designed as per the requirement of the system which is ready to be installed to analyse the data technically. In the previous stage of data exploration, we used the graphical representation of data to make it easy to understand the trends and pattems. But when it comes to machines, it only understands the language of 1 seconds and Os so they only rely on mathematical representation of data. Data Modelling. 

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An important stage in the process of Al project cycle where we decide on the technique to be followed for building a model from the prepared data. It is a mathematical approach in which an algorithm is designed as per the requirement of the system which is ready to be installed to analyse the data technically. In the previous stage of data exploration, we used the graphical representation of data to make it easy to understand the trends and pattems. But when it comes to machines, it only understands the language of 1s and Os so they only rely on mathematical representation of data.

[Audio] Rule Based Approach This approach is based on a set of rules and facts defined by the developer and fed to the machine to perform its task accordingly to generate the desired output. These models can operate with simple basic information and data The drawback of this approach is that the leaming for the machine is static, as once trained, the machine does not take into consideration any changes made in the original training dataset. If the machine is tested on a different dataset from the rules and the data fed in at the training stage, the machine will fail and will not learn from the new conditions encountered.. 

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This approach is based on a set of rules and facts defined by the developer and fed to the machine to perform its task accordingly to generate the desired output. These models can operate with simple basic information and data
 The drawback of this approach is that the leaming for the machine is static, as once trained, the machine does not take into consideration any changes made in the original training dataset. If the machine is tested on a different dataset from the rules and the data fed in at the training stage, the machine will fail and will not learn from the new conditions encountered.

[Audio] Learning Based Approach This approach refers to the model where the relationship or patterns in the data are not defined by the developer. Random data is fed into the machine and the machine develops its own pattern or trends based on data outputs. This approach is considered to take care of the challenges of rule-based systems. For example, suppose you have a dataset of 1000 images of flowers. Now you do not have any clue as to what trend is being followed in this dataset as you don't know their names, colour or any other feature. Thus, you would put this into a learning approach-based Al machine and the machine would come up with various patterns it has observed in the features of these 1000 images.. 

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This approach refers to the model where the relationship or patterns in the data are not defined by the developer. Random data is fed into the machine and the machine develops its own pattern or trends based on data outputs.
 This approach is considered to take care of the challenges of rule-based systems. For example, suppose you have a dataset of 1000 images of flowers. Now you do not have any clue as to what trend is being followed in this dataset as you don't know their names, colour or any other feature. Thus, you would put this into a learning approach-based Al machine and the machine would come up with various patterns it has observed in the features of these 1000 images.

[Audio] Accuracy Accuracy is the percentage of correct prediction out of the total observations made in an (A-I ) model. It gives you a clear picture on how accurate is the prediction for the given (A-I ) Model. High Accuracy means good performance of the Al model as accuracy counts all of the true predicted values. Precision Precision is the percentage of True Positive cases and All Predicted Positive Cases. Recall Recall is defined as the fraction of positive cases that are correctly identified. It majorly takes into account the true reality cases in other words; it is a measure of our model correctly identifying True Positives. F1 score also called F-score or F-measure is the measure of a test's accuracy. It can be defined as the measure of balance between precision and recall. The F1 score is a number between 0 and 1 and is the harmonic mean of precision and recall Evluation. 

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Accuracy
 Accuracy is the percentage of correct prediction out of the total
  observations made in an AI model. It gives you a clear picture 
 on how accurate is the prediction for the given AI Model. High
  Accuracy means good performance of the Al model as accuracy
  counts all of the true predicted values.


 Precision
 Precision is the percentage of True Positive cases and All 
 Predicted Positive Cases.

 Recall
 Recall is defined as the fraction of positive cases that are 
 correctly identified. It majorly takes into account the true reality
  cases i.e.; it is a measure of our model correctly identifying

  True Positives.
 F1 score also called F-score or F-measure is the measure of a 
 test's accuracy. It can be defined as the measure of balance
  between precision and recall. The F1 score is a number
  between 0 and 1 and is the harmonic mean of precision and
  recall

[Audio] Neural NetWorks Neural Networks form a base of Deep learning, a subfield of Machine learning where algorithms are inspired by the structure of the human brain. Neural networks take in data, train themselves to recognise the patterns in this data and then predict the outputs for a new set of similar data. The most impressive aspect of neural networks is that once trained, they leam on their own just like human brain. 

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Neural Networks form a base of Deep learning, a subfield of Machine learning where algorithms are inspired by the structure of the human brain. Neural networks take in data, train themselves to recognise the patterns in this data and then predict the outputs for a new set of similar data. The most impressive aspect of neural networks is that once trained, they leam on their own just like human brain

[Audio] Thank You For Watching By A V Amarnath X/a 30 hours. 

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Blue Futuristic Artificial Intelligence Presentation

Ai Project
        Cycle
 

 By  A V Amarnath

avatar

The (A-I ) Project Cycle is a structured process that
  guides the creation and deployment of (A-I ) solutions.
  It involves the following stages :

  1 Problem Scoping:   Identifying and understanding
  the problem that needs solving with (A-I ). This
  includes defining objectives and what success looks
  like.
  2 Data Acquisition: Gathering data relevant to the
  problem, which can come from various sources like
  surveys, sensors, or existing datasets.

  3 Data Exploration: Analysing the data to 
 understand its structure, quality, and potential
  patterns. This is where we clean and organize the
  data to make it ready for (A-I ) models.

 4 Modelling: Choosing and applying appropriate 
 algorithms to build an (A-I ) model. This is where the (A-I )
  learns from the data.

  5 Evaluation: Testing the model to see how
  accurately it solves the problem, using metrics to
  measure its performance

Problem Scoping
 v
 v
 v
 

 v
 This is the first and the crucial stage of (A-I ) project development which focuses on identifying and understanding problems using 4Ws—Who, What, Where and Why.
 It is the analytics approach that involves taking steps to solve the problems and setting up goals that we want our project to achieve.
 Scoping a problem is not that easy as we need to have a deeper understanding around it so that we have clarity as to what we want our project to achieve while we are working to solve it. Hence, we use the A-W-S Problem Scoping Canvas to help us out.

We are surrounded by problems. We are so used to these problems around us that they seem to be part of our lives. When we cannot observe a problem around us, then we should refer to the 17 goals that have been announced by the United Nations as the Sustainable Development Goals. These goals are to be achieved by 2030 as pledged by member nations of the UN. Artificial Intelligence supported solutions are suggested to assist society and government to achieve these goals that would work to improve the lives of the people living in poverty all across the nations.




 Sustainable Development Goals

This is the second stage of the (A-I ) project cycle. It involves collecting data required for training the (A-I ) model. Data is raw information that is used to generate meaningful outcomes.
 For example, if we want to create an (A-I ) system to predict traffic flow for a specific geographic area, we would need previous traffic data. This data is essential for training the system. The training data allows the (A-I ) model to make predictions about future traffic patterns. The data from the previous year or past periods, which is used to feed into the system, is known as Training Data. The data used to test the accuracy of these predictions is known as Testing Data.
 The efficiency of the (A-I ) system depends on the authenticity and relevance of the training data. If the traffic data used is inaccurate, or if it's for a different location or time, then the AI’s predictions will not be reliable.
 To ensure effective performance, it is crucial to use relevant and accurate data, aligned with the problem statement.
 Data  Acquisition

Data Exploration
 

 This is the third stage in the Al project cycle. It refers to exploring the large data to uncover the patterns or trends needed for the Al project.
 It is considered to be the first step in data analysis where unstructured data is explored, researched, filtered and visualised to decide the strategy for the type of model used in the later stage.

An important stage in the process of Al project cycle where we decide on the technique to be followed for building a model from the prepared data. It is a mathematical approach in which an algorithm is designed as per the requirement of the system which is ready to be installed to analyse the data technically. In the previous stage of data exploration, we used the graphical representation of data to make it easy to understand the trends and pattems. But when it comes to machines, it only understands the language of 1 seconds and Os so they only rely on mathematical representation of data.
 Data Modelling

Rule Based Approach




 




 

 
 This approach is based on a set of rules and facts defined by the developer and fed to the machine to perform its task accordingly to generate the desired output. These models can operate with simple basic information and data
 The drawback of this approach is that the leaming for the machine is static, as once trained, the machine does not take into consideration any changes made in the original training dataset. If the machine is tested on a different dataset from the rules and the data fed in at the training stage, the machine will fail and will not learn from the new conditions encountered.

Learning Based Approach


 

 This approach refers to the model where the relationship or patterns in the data are not defined by the developer. Random data is fed into the machine and the machine develops its own pattern or trends based on data outputs.
 This approach is considered to take care of the challenges of rule-based systems. For example, suppose you have a dataset of 1000 images of flowers. Now you do not have any clue as to what trend is being followed in this dataset as you don't know their names, colour or any other feature. Thus, you would put this into a learning approach-based Al machine and the machine would come up with various patterns it has observed in the features of these 1000 images.

Accuracy
 Accuracy is the percentage of correct prediction out of the total
  observations made in an (A-I ) model. It gives you a clear picture 
 on how accurate is the prediction for the given (A-I ) Model. High
  Accuracy means good performance of the Al model as accuracy
  counts all of the true predicted values.


 Precision
 Precision is the percentage of True Positive cases and All 
 Predicted Positive Cases.

 Recall
 Recall is defined as the fraction of positive cases that are 
 correctly identified. It majorly takes into account the true reality
  cases in other words; it is a measure of our model correctly identifying

  True Positives.
 F1 score also called F-score or F-measure is the measure of a 
 test's accuracy. It can be defined as the measure of balance
  between precision and recall. The F1 score is a number
  between 0 and 1 and is the harmonic mean of precision and
  recall
 


 Evluation

Neural NetWorks
 

 
 

 Neural Networks form a base of Deep learning, a subfield of Machine learning where algorithms are inspired by the structure of the human brain. Neural networks take in data, train themselves to recognise the patterns in this data and then predict the outputs for a new set of similar data. The most impressive aspect of neural networks is that once trained, they leam on their own just like human brain

Thank You
 For Watching
 By A V Amarnath       X/a    30 hours