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i. Your model must detect patterns in images.
ii. False forecasts can have disastrous effects. Errors can be fatal in such a case.
iii. It may take up to an hour to reveal the outcome; so the motto "Take your time, but be precise." must be followed.
an you see ho different cases result in different demands he desired output type also shifts. In the first scenario, you
ant a te t output in the second, you ant a categorisation classification the patient is healthy or not.
coping a problem is difficult because e need to have a deeper understanding of it so that the image gets clearer as
we attempt to solve it. As a result, we employ the 4Ws Problem Canvas to assist us in identifying the important factors
associated with the problem.
Using the 4Ws problem canvas, the problem statement can be formulated as follows:
The [the stakeholders] Who
have a problem that [issue, need] What
when/while [situation/location] Where
A good solution using AI would solution benefit Why
Hence, in the future, whenever there is a need to look at the foundation of the problem again, we can take a look at the
Problem Statement Template to understand its key elements.
Experiential Learning
Video Session
Scan the QR code or visit the following link to watch the video: 4Ws of Problem Scoping in AI Project
Cycle | The 4Ws Canvas | Aiforkids
https://www.youtube.com/watch?v=qFoIxZPt9Ho
After watching the video, answer the following question:
What have you learnt from this video?
Data A cq uisition
o undertake data analysis, one must first collect data from credible sources. eal orld data can be strange and
deceptive. Human entries are always prone to mistakes, such as someone mistyping 40.0 as 400, someone spelling
something, or labeling the data incorrectly. Hence, the data needs to be relevant and authentic. Authentic data can be
gathered from a variety of sources, including:
• government websites • devices such as cameras and sensors
• purchases, transactions, registrations • other public surveys and records
Data Ex plor ation
After gathering data, processes such as:
• data cleaning to locate missing values
• eliminating worthless data (erroneous samples and outliers)
To know more about data cleaning techniques go through the following link:
https://towardsdatascience.com/data-cleaning-techniques-in-microsoft-excel-you-need-to-know-
4075cbb30731
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