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Below are few questions where I unable to find out where I am wrong as my submitted questions with these answer were wrong. I added screen shot of image and explanations of the each options that I am understanding. Questions are purely discussion based and short.

I posted this question to know more about these topics in deep and how it works.

Please help me out.

A. We need to predict the author gender and it can be either male or female. I think it is classification problem, so it is supervised learning.

B. We have given group of spam emails and need to predict does there sub-types are spam or not. I think it is classification problem, so it is supervised learning.

C. We need to predict data based on height and age. It is a linear regression problem because we create graph height vs age will find out the test case. It is supervised learning.

D. Grouping data is a cluster problem, so it unsupervised learning.

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In below question, I had checked C and D options because feature scaling creates our dataset in same range which helps to predict the best theta in less iterations and contour graph will be more cleared and symmetric. Ref : https://medium.com/greyatom/why-how-and-when-to-scale-your-features-4b30ab09db5e

enter image description here

dahiya_boy
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    In second question, I find option A to be true. we apply normalization to improve accuracy not the speed. Large scaled features create large variance which is more comparable to small values features. That's why we bring down all features in the same scale. – Rheatey Bash May 07 '19 at 20:57
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    In first question, I don't think Option B is a classification problem as described in the option. It is a clustering problem based on the featire vector nearest to the already given set of spam emails. – Rheatey Bash May 07 '19 at 21:01

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