Day 4 - Linear Regression Numpy code and Python course

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Linear Regression Numpy code 

 We finished coding generating data for the numpy version of Linear regression.
We didn't use data from an excel sheet or a Kaggle dataset and hence we had to create our own data. For this, we created random integer data for our X and betas. Then we created a noise, as real data always has noise, using this we created Y data.

the code for the same was as follows:

import numpy as np
samplesize=1000
num_attrs= 3
step = 0.1

 x_inputs = np.random.rand(samplesize,num_attrs-1)
x0 = np.ones((samplesize,1))
x_data = np.concatenate((x0, x_inputs), axis=1)

noise = np.random.randn(len(x_inputs),1) 

betas = np.random.rand(num_attrs,1)

y_true = x_data.dot(betas) + noise  #understand this
y_true.reshape(1000,1)

Python course

 We started an Udemy course on Python.
The concepts we covered today were:
  1. Pros and Cons of Dynamic Typing
  2. String Indexing and Slicing
  3. Various String Methods
  4. String Interpolation: 
          a) format()
          b) Float formatting
          c) Formatting with String literals
          d) Alignment, padding, and precision with format()

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