Ai project cycle

Question 1

What is Testing Dataset?

Answer 1

The dataset provided to the model ML. algorithm after training the algorithm

Question 2

Mention the types of learning approaches for AI modeling.

Answer 2

Supervised, unsupervised and re-enforcement

Question 3

What is the objective of evaluation stage?

Answer 3

It is to evaluate whether the ML algorithm is able to predict with high accuracy or not
before deployment.

Question 4

The analogy of an Artificial Neural Network can be made with _____________?

Answer 4

Parallel processing

Question 5

Neural network is a mesh of multiple ____

Answer 5

Hidden layers or layers

Question 6

Explain the sdg no poverty

Answer 6

This is Goal 1 and strives to End poverty in all its forms everywhere globally by 2030. The goal has a total of seven targets to be achieved.

Question 7

Explain quality education

Answer 7

This is Goal 4 which aspires to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. It has 10 targets to achieve.

Question 8

Explain classification ai model

Answer 8

-In classification, data is categorized under different labels according to some parameters given in input and then the labels are predicted for the data.
- data is not continuous
- labeled data

Question 9

Explain regression ai model

Answer 9

Regressionis the process of finding a model for distinguishing the data into continuous real values instead of using discrete values. It can also identify the distribution movement depending on the historical data.
- continuous data

Question 10

Explain clustering ai model

Answer 10

It refers to the unsupervised learning algorithm which can cluster the unknown data according to the patterns or trends identified out of it. The patterns observed might be the ones which are known to the developer or it might even come up with some unique patterns out of it.

Question 11

The stage that deals with validating and verification of the collected data

Answer 11

Data exploration

Question 12

Features of ann

Answer 12

Any Artificial Neural Network, irrespective of the style and logic of implementation, has a few basic features as given below.
The Artificial Neural Network systems are modelled on the human brain and nervous system.
They are able to automatically extract features without feeding the input by programmer.
Every node of layer in a Neural Network is compulsorily a machine learning algorithm. It is very useful to implement when solving problems for very huge datasets.

It can work with incomplete knowledge and may produce output even with incomplete information.
It has fault tolerance which means that corruption of one or more cells of ANN does not prevent it from generating output.
It has the ability to learn events and make decisions by commenting on similar events.
It has Parallel processing capability i.e. ANN have numerical strength that can perform more than one job at the same time.

Question 13

What is the purpose of getting ai ready

Answer 13

The purpose of getting AI ready means taking steps to collect data around relevant systems, equipment, and procedures; and storing and curating that data in a way that makes it easily accessible to others for use in future AI applications.

The purpose of getting AI ready specifies the responsible and optimum use of huge
amount of data around us to create and implement into such systems and applications which should make life of future generations more organized and sustainable. This process may lead to better lives for mankind.

Question 14

Define data

Answer 14

Data can be a piece of information or facts and statistics collected together for reference or analysis.

Question 15

Ways of collecting data

Answer 15

There could be many ways and sources from where we can collect reliable and authentic datasets namely Surveys, Web scrapping, Sensors, Cameras, Observations, Research, Investigation, API etc.
Sometimes Internet is also used to acquire data but the most important point to keep in mind is that the data should be taken from reliable and authentic websites only. Some reliable data sources are UN, Google scholar, Finance, CIA, Data.gov etc.

Question 16

Explain the relation between data size and model performance of an Artificial Neural Network.

Answer 16

The basis for any kind of AI development is BIG DATASET. The performance of any AI based application depends on the data supplied ANN models are also known as Learning models and are used for prediction purposes. These are mostly developed without paying much cognizance to the size of datasets that can produce models of high accuracy and better generalization.
Although, the general belief is that, large dataset is needed to construct a predictive learning model. To describe a data set as large in size, perhaps, is circumstance dependent, thus, what constitutes a dataset to be considered as being big or small is somehow vague.
In fact, the quantity of data partitioned for the purpose of training must be of good representation of the entire sets and sufficient enough to span through the input space. It must be authentic and relevant to give better model performance.

Question 17

What is an Artificial Neural Network?

Answer 17

Artificial Neural Network: Modeled in accordance with the human brain, a Neural
Network was built to mimic the functionality of a human brain. The human brain is a
neural network made up of multiple neurons, similarly, an Artificial Neural Network
(ANN) is made up of multiple perceptrons.

Question 18

Explain the layers in an artificial neural network.

Answer 18

A neural network consists of three important layers:
Input Layer: As the name suggests, this layer accepts all the inputs provided by the programmer.
Hidden Layer: Between the input and the output layer is a set of layers known as Hidden layers. In this layer, computations are performed which result in the output. There can be any number of hidden layers
Output Layer: The inputs go through a series of transformations via the hidden layer which finally results in the output that is delivered via this layer.

Question 19

What is the need of an AI Project Cycle? Explain.

Answer 19

Project cycle is the process of planning, organizing, coordinating, and finally developing a project effectively throughout its phases, from planning through execution then completion and review to achieve pre-defined objectives.
- if we have to develop an AI project, the AI Project Cycle provides us with an appropriate framework which can lead us towards the goal.
The major role of AI Project Cycle is to distribute the development of AI project in various stages so that the development becomes easier, clearly understandable and the steps / stages should become more specific to efficiently get the best possible output. It mainly has 5 ordered stages which distribute the entire development in specific and clear steps: These are Problem Scoping, Data Acquisition, Data Exploration, Modelling and Evaluation.

Question 20

What is supervised learning

Answer 20

Supervised learning is an approach to creating artificial intelligence (AI), where the program is given labelled input data and the expected output results.

Question 21

What is unsupervised learning

Answer 21

Unsupervised learning is the training of a machine using information that is neither classified nor labelled and allowing the algorithm to act on that information without guidance. Here the task of the machine is to group unsorted information according to similarities, patterns and differences without any prior training of data.

Question 22

Difference between classification and regression

Answer 22

The Classification process models a function through which the data is predicted in discrete class labels. On the other hand, regression is the process of creating a model which predicts continuous quantity.
 The classification algorithms involve decision tree, logistic regression, etc. In contrast, regression tree (e.g. Random forest) and linear regression are the examples of regression algorithms.
 Classification predicts unordered data while regression predicts ordered data.
 Regression can be evaluated using root mean square error. On the contrary,
classification is evaluated by measuring accuracy.