Week 14 ( 01/10/2018 - 05/10/2018)

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Day 1 - Numpy Revision

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Numpy Revision  We made a python program for the following random walk question: Say you are standing at the bottom of a staircase with a dice. With each throw of the dice, you either move down one step (if you get a 1 or 2 on the dice) or move up one step (if you get a 3, 4 or 5 on the dice). If you throw a 6 on the dice, you throw the dice again and move up the staircase by the number you get on that second throw. Note if you are on the base of the staircase you cannot move down! What is the probability that you will reach more than 60 steps after 250 throws of the dice? This question required the knowledge of np.random.seed. It s eeds the generator.  It makes the random numbers predictable. When the value is reset, the same numbers will appear every time. If a  seed is  not assigned , NumPy automatically selects a random seed value based on the system's random number generator device or on the clock The code was as follows: import numpy as np n...
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Week 2 ( 9/7/18 - 13/7/18)

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                                                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...
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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: Pros and Cons of Dynamic Typing String Indexing and Slicing Various String Methods String Interpolation:  ...
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