Week 2 (15-19 july)

                                 Day1

 In today's udemy course we learned about following topics:

  • Methods and functions
  • *args and **kwargs
  • lambda expressions , maps and filters
Along with this we solved few coding excercises related to this and decided to work on udemy course milestone project-1.

                                                 Day2

 Today, firstly we discussed about the milestone project and sorted out the problems related to it.After that we continued with our python course and learnt about object oriented programming in python which includes following topics:
  • Class object attributes and methods
  • Inheritance and polymorphism
  • Special(Magic/Dunder) Methods
and solved some homework excercises related to it.

                                                Day3

Today we started with the scikit-learn implementation of linear regression model and compared the results of this model with previously implemented code of linear regression from scratch on a sample data set given by Vikram sir.

Later we started with modules and packages (we need them to run different - different functionality) in python course.After that we learnt about exception handling in python.

                                                 Day4

 Today firstly we were asked to implement the logistic regression from scratch using numpy.The code for the same is shown below:

 import numpy as np
import math
sample=1000

features=3

 x_data=np.random.rand(sample,features)
x_ones=np.ones((sample,1))
x_initial=np.append(x_ones,x_data,axis=1)
beta=np.random.rand(features+1,1)
noise=np.random.randn(sample,1)

 #y sigmoid
z0=np.dot(x_initial,beta)+noise
e_z0=np.exp(-z0)
y_initial= np.ones((sample,1))
for i in range(len(z0)):
    
    y_initial[i]=(1/(1+e_z0[i]))
y_initial=np.round(y_initial)
beta=np.random.rand(features+1,1)

z=np.dot(x_initial,beta)
For today we only generated the data and found the sigmoid value.
After this we shifted towards python decorators and generators in the python course.

                                                   Day5
Today firstly we completed our code of logistic regression which is as follows:

 beta=np.random.rand(features+1,1)
for i in range(1000):
      y_pred=np.ones((sample,1)) #it is same as p or probability or sigmoid
    e_z=np.exp(-z)
    for i in range(len(e_z)):
        y_pred[i]=1/(1+e_z[i])
    derivative=(np.dot(np.transpose(x_ones),y_initial-y_pred))/sample
    beta=beta+0.01*derivative
    cost=-(y_initial*np.log(y_pred)+(x_ones-y_initial)*np.log(x_ones-y_pred))
    z=np.dot(x_initial,beta)

print(np.mean(cost))

 After this we decided to do milestone project-2 which was to develop a blackjack game in python and discussed algorithm for the same.

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