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

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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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Week 19 and 20 : 1/11/2018-16/11/2018

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Time Series Forecasting We had sessions with our mentor Varun Goel from Tatras,Delhi throughout the week and so on different techniques used in forecasting. We covered following topics in these sessions: ● Forecasting – Introduction ● Linear Regression ● Moving Averages ● Exponential Smoothing ● ARMA, ARIMA ● Neural Networks, BRANNs What is forecasting? Forecast: (verb) to calculate or predict (some future event or condition) usually as a result of study and analysis of available pertinent data Things we forecast… – Weather parameters – Sales numbers/ Demand – Agricultural Production – Currency exchange rates, Stock prices, Gold prices, Interest rates – Internet Traffic – Loss due to Epidemics and Natural Disasters and wars Forecasting for  a Stationary Time Series A stationary time series has the form: D t = m + e t  ,  where m is a constant and e t is a random variable with mean 0 and var σ 2  Methods: ● Naiv...
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Week 18 (29/10/2018 - 02/11/2018)

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Ropar's Accident data Our company was given Ropar's Accident data by the Government to analyze and submit insights from the data that could be helpful to the government for reducing the number of serious accidents. We analyzed the data by using Tableau with the help of pie charts, histograms, etc. We also made dashboards and stories which gave us a really good insight of the data.   Convolutional Neural Networks We referred to the CMU's course on deep learning for understanding this concept.This lecture was taught by Bhiksha Raj Sir. CNNs, like neural networks, are made up of neurons with learnable weights and biases. Each neuron receives several inputs, takes a weighted sum over them, pass it through an activation function and responds with an output. Unlike neural networks, where the input is a vector, here the input is a multi-channeled image. The wholw network has a loss function. CNNs have wide applications in image and video recognition, re...
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