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

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Week 2 ( 9/7/18 - 13/7/18)

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Week 12 and 13: 17/9/2018 - 30/9/2018

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Anomaly Detection    Session with Satnam Singh sir  All the interns had a 2 hour long session with Dr.  Satnam Singh ,chief data scientist, Acalvio technologies, Bengaluru, India. In the session, he discussed various points and issues related to cyber security and online frauds and he shared some domain knowledge on the related topics and his team's work. It was an interactive session and he also asked about the work interns were doing. He shared his experience which benefited students and we learned some new approaches and terminology. Problem statement Satnam sir shared a  kaggle problem  and asked all of us to work on it. It was a credit card fraud detection problem and was to be solved as an anomaly detection problem with statistical way without using any libraries such as scikit-learn etc. Sir gave us ample amount of time to work on it before he would review all our progress and code. So after the session, we started exploring different...
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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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