Amritesh kumar im-noob

## millions Request with payload.py
'''
  Reuire More than 12 GB ram to process all the request..
  !pip install nest-asyncio
'''

import asyncio
from aiohttp import ClientSession
import nest_asyncio
nest_asyncio.apply()

## 2-ml-exercises.md

      
        
          
            
              
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                byelipk
                / 2-ml-exercises.md
            
            
              Last active
              April 2, 2024 13:59
            
              
                Machine learning questions and answers
              
          
        
      
        
  
      
    Exercises

1. What Linear Regression training algorithm can you use if you have a training set with millions of features?

You could use batch gradient descent, stochastic gradient descent, or mini-batch gradient descent. SGD and MBGD would work the best because neither of them need to load the entire dataset into memory in order to take 1 step of gradient descent. Batch would be ok with the caveat that you have enough memory to load all the data.
The normal equations method would not be a good choice because it is computationally inefficient. The main cause of the computational complexity comes from inverse operation on an (n x n) matrix.
O n2 . 4   to O n3
2. Suppose the features in your training set have very different scales: what algorithms might suffer from this, and how? What can you do about it?
	'''
	Reuire More than 12 GB ram to process all the request..
	!pip install nest-asyncio
	'''

	import asyncio
	from aiohttp import ClientSession
	import nest_asyncio
	nest_asyncio.apply()