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A machine be capable of learning be built through: By building a machine with sufficient computational resources, offering training examples from real-world data, and by designing specific algorithms and tools that define a learning process, rather than specific data manipulations, machines can imprRead more
A machine be capable of learning be built through:
By building a machine with sufficient computational resources, offering training examples from real-world data, and by designing specific algorithms and tools that define a learning process, rather than specific data manipulations, machines can improve their own performance through learning by doing, inferring patterns, and hypothesis checking.
By reinforcement learning that uses external feedback to teach the system to change its internal workings in order to guess better next time. This self-change involves identifying the factors that made the biggest difference in the algorithm’s decision, reinforcing accuracy and discouraging wrong decisions.
ANSWER: This problem statement can be solved using the KNN algorithm, which will classify the applicant’s loan request into two classes: Approved Disapproved K Nearest Neighbour is a Supervised Learning algorithm that classifies a new data point into the target class, depending on the features of itRead more
ANSWER:
This problem statement can be solved using the KNN algorithm, which will classify the applicant’s loan request into two classes:
Approved
Disapproved
K Nearest Neighbour is a Supervised Learning algorithm that classifies a new data point into the target class, depending on the features of its neighboring data points.
The following steps can be carried out to predict whether a loan must be approved or not:
Data Extraction: At this stage data is either collected through a survey or web scraping is performed. Data about the customers must be collected. This includes their account balance, credit amount, age, occupation, loan records, etc. By using this data, we can predict whether or not to approve the loan of an applicant.
Data Cleaning: At this stage, the redundant variables must be removed. Some of these variables are not essential in predicting the loan of an applicant, for example, variables such as Telephone, Concurrent credits, etc. Such variables must be removed because they will only increase the complexity of the Machine Learning model.
Data Exploration & Analysis: This is the most important step in AI. Here you study the relationship between various predictor variables. For example, if a person has a history of unpaid loans, then the chances are that he might not get approval on his loan applicant. Such patterns must be detected and understood at this stage.
Building a Machine Learning model: There are n number of machine learning algorithms that can be used for predicting whether an applicant loan request is approved or not. One such example is the K-Nearest Neighbor, which is a classification and a regression algorithm. It will classify the applicant’s loan request into two classes, namely, Approved and Disapproved.
Model Evaluation: Here, you basically test the efficiency of the machine learning model. If there is any room for improvement, then parameter tuning is performed. This improves the accuracy of the model.
QUESTION 1. How alpha beta search is different from minimax search algorithm. How does it overcome the problem of minimax search algorithm. ANSWER: Alpha-beta pruning is a procedure to reduce the amount of computation and searching during minimax. Minimax is a two-pass search, one pass is used to asRead more
QUESTION 1.
How alpha beta search is different from minimax search algorithm. How does it overcome the problem of minimax search algorithm.
ANSWER:
Alpha-beta pruning is a procedure to reduce the amount of computation and searching during minimax. Minimax is a two-pass search, one pass is used to assign heuristic values to the nodes at the ply depth and the second is used to propagate the values up the tree.
The Alpha-beta pruning to a standard minimax algorithm returns the same move as the standard algorithm does, but it removes all the nodes which are not really affecting the final decision but making algorithm slow. Hence by pruning these nodes, it makes the algorithm fast.
QUESTION 2.
The crop yield in India is degrading because farmers are unable to detect diseases in crops during the early stages. Can AI be used for disease detection in crops? If yes, explain.
ANSWER:
Plant diseases are one of the most important reasons that lead to the destruction of plants and crops. Detecting those diseases at early stages enable farmers to overcome and treat them appropriately.
Artificial Intelligence in Agriculture not only helps farmers to use their farming skills but also shifts to direct farming to get higher yields and better quality with
less resources.
Using of AI tools/methods help farmers to diagnose their farm.
Techniques such as:
Drone-based images can help in crop monitoring, scanning of fields and so on. Farmers can join them with PC vision innovation and IOT to guarantee quick activities. These feeds can produce ongoing climate alarms for farmers.
Also,
The image sensing and analysis make sure that the plant leaf images are segmented into surface areas like background, diseased area and non-diseased area of the leaf. The infected or diseased area is then harvested and sent to the laboratory for additional diagnosis.
Remote sensing (RS) techniques along with hyperspectral imaging and 3D laser scanning are crucial to constructing crop metrics over thousands of acres of cultivable land.
Suppose you are planning to develop a project on employee attendance. Design a project plan by using following scenario This …
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See lessConsider the scenario A Online bookstore is to be implemented. This project is a website that acts as a central …
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See lessHow can a machine be capable of learning be built?
A machine be capable of learning be built through: By building a machine with sufficient computational resources, offering training examples from real-world data, and by designing specific algorithms and tools that define a learning process, rather than specific data manipulations, machines can imprRead more
A machine be capable of learning be built through:
- By reinforcement learning that uses external feedback to teach the system to change its internal workings in order to guess better next time. This self-change involves identifying the factors that made the biggest difference in the algorithm’s decision, reinforcing accuracy and discouraging wrong decisions.
See lessA bank manager is given a dataset containing 1000 records of applicants who have applied for loan. How can AI …
ANSWER: This problem statement can be solved using the KNN algorithm, which will classify the applicant’s loan request into two classes: Approved Disapproved K Nearest Neighbour is a Supervised Learning algorithm that classifies a new data point into the target class, depending on the features of itRead more
ANSWER:
This problem statement can be solved using the KNN algorithm, which will classify the applicant’s loan request into two classes:
K Nearest Neighbour is a Supervised Learning algorithm that classifies a new data point into the target class, depending on the features of its neighboring data points.
The following steps can be carried out to predict whether a loan must be approved or not:
Data Extraction: At this stage data is either collected through a survey or web scraping is performed. Data about the customers must be collected. This includes their account balance, credit amount, age, occupation, loan records, etc. By using this data, we can predict whether or not to approve the loan of an applicant.
Data Cleaning: At this stage, the redundant variables must be removed. Some of these variables are not essential in predicting the loan of an applicant, for example, variables such as Telephone, Concurrent credits, etc. Such variables must be removed because they will only increase the complexity of the Machine Learning model.
Data Exploration & Analysis: This is the most important step in AI. Here you study the relationship between various predictor variables. For example, if a person has a history of unpaid loans, then the chances are that he might not get approval on his loan applicant. Such patterns must be detected and understood at this stage.
Building a Machine Learning model: There are n number of machine learning algorithms that can be used for predicting whether an applicant loan request is approved or not. One such example is the K-Nearest Neighbor, which is a classification and a regression algorithm. It will classify the applicant’s loan request into two classes, namely, Approved and Disapproved.
Model Evaluation: Here, you basically test the efficiency of the machine learning model. If there is any room for improvement, then parameter tuning is performed. This improves the accuracy of the model.
See lessQUESTION 1 How alpha beta search is different from minimax search algorithm. How does it overcome the problem of minimax …
QUESTION 1. How alpha beta search is different from minimax search algorithm. How does it overcome the problem of minimax search algorithm. ANSWER: Alpha-beta pruning is a procedure to reduce the amount of computation and searching during minimax. Minimax is a two-pass search, one pass is used to asRead more
QUESTION 1.
How alpha beta search is different from minimax search algorithm. How does it overcome the problem of minimax search algorithm.
ANSWER:
QUESTION 2.
The crop yield in India is degrading because farmers are unable to detect diseases in crops during the early stages. Can AI be used for disease detection in crops? If yes, explain.
ANSWER:
Plant diseases are one of the most important reasons that lead to the destruction of plants and crops. Detecting those diseases at early stages enable farmers to overcome and treat them appropriately.
Artificial Intelligence in Agriculture not only helps farmers to use their farming skills but also shifts to direct farming to get higher yields and better quality with
less resources.
Using of AI tools/methods help farmers to diagnose their farm.
Techniques such as:
Drone-based images can help in crop monitoring, scanning of fields and so on. Farmers can join them with PC vision innovation and IOT to guarantee quick activities. These feeds can produce ongoing climate alarms for farmers.
Also,
The image sensing and analysis make sure that the plant leaf images are segmented into surface areas like background, diseased area and non-diseased area of the leaf. The infected or diseased area is then harvested and sent to the laboratory for additional diagnosis.
Remote sensing (RS) techniques along with hyperspectral imaging and 3D laser scanning are crucial to constructing crop metrics over thousands of acres of cultivable land.
See lessConsider software for academic activities of a university. The project will cover activities like managing of students as well as …
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VOTE UP ANSWERS AND SUPPORT US !
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