Liner regression and feature scaling Below are few questions where I unable to find out where I am wrong. I added screen shot of image and explanations of the each options that I am understanding. Questions are purely discussion based and short. Please help me out.



In below question, I checked 

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. 





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
For the 1st question, I think B is not a supervised learning problem. You are already given emails which are spam. You need to analyse them to find if there are subtypes (Clustering.)

For the 2nd question, it is just B. Scaling doesn't make each step less computationally expensive. You do same amount of computation.