Page 167 - AI Ver 1.0 Class 10
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4.  Explain Data Exploration stage.
                  Ans.   This is the third stage in the AI project cycle. It refers to exploring the large data to uncover the patterns or trends
                       needed for the AI 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.
                    5.  Write four important features of a neural networks.
                  Ans.  The four important features of neural networks are:
                       •  The model of the AI neural network is based on the human neural network i.e., brain and nervous system.
                       •  They are designed in such a way that the information can be automatically extracted without the interaction of the
                        programmer.
                       •  Every node of a neural network system is a Machine Learning algorithm.
                       •  It is best suited for AI models dealing with large data.

                B.  Long answer type questions:
                    1.  What are sustainable development goals?
                  Ans.   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 the society and government to
                       achieve these goals that would work to improve the lives of the people living in the society all across the nations.
                    2.  What is regression?
                  Ans.   Regression is an example of rule-based AI model. In regression, the algorithm generates a mapping function from the
                       given data. With the help of this mapping function, we can predict the future data. For example, if we want to predict
                       the temperature of a day in a year, we can use past year’s temperature for that day as training data and can predict it for
                       the coming year. Regression is a mathematical approach to find a relationship between two or more variables. It works
                       with continuous data. This can be used for weather forecasting, time series modelling, etc. In order to get the best fit
                       results, the distance between the line and data points should be minimum.
                    3.  Why do we use Neural Networks?
                  Ans.   Neural Networks are a series of algorithms used to recognise hidden patterns in raw data, cluster and classify it, and
                       continuously learn and improve. They are used in a variety of applications in stock markets, sales and marketing trends,
                       risk assessment and fraud detection. The main advantage is that the data features can be extracted automatically by
                       the machine without the input from the developer. Neural networks are primarily used for solving problems with large
                       datasets, like images.
                    4.  Explain unsupervised learning model.
                  Ans.   An unsupervised learning approach works on an unlabeled dataset. This means that the data which is fed to the
                       machine is random and there is no knowhow available about it to the trainer.
                          These learning models are used to identify trend, pattern and relationship in the data which is fed into it. In this model
                       the major features are identified by the machine, which helps the user in understanding the data. For example, in the
                       data of 100 cat images, if you want to understand some pattern in the data, you would need to feed this data into the
                       unsupervised learning model and train the machine. Once trained, the machine would identify patterns in the data.
                       These patterns might already be known to the user, like colour or size, or something unusual about the cats.
                    5.  What is Evaluation. Describe the process involved in it.
                  Ans.   Evaluation is a very important stage of AI Project designing and training where we properly test the system to find out
                       the efficiency and performance of the model. After the model is designed and trained then the reliability of the model
                       is checked using Testing Data acquired at the Data Acquisition stage. This testing data is given as an input to the newly
                       created AI model and the output received is checked and evaluated on the basis of:
                       •  Accuracy
                       •  Precision
                       •  Recall
                       •  F1 score





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