The following problems are taken from the projects / assignments in the edX course Python for Data Science and the coursera course Applied Machine Learning in Python (UMich).

1. Exploratory Analysis to Find Trends in Average Movie Ratings for different Genres

Dataset

● The IMDB Movie Dataset (MovieLens 20M) is used for the analysis.

● The dataset is downloaded from …

he following problems are taken from a few assignments from the coursera courses Introduction to Deep Learning (by Higher School of Economics) and Neural Networks and Deep Learning (by Prof Andrew Ng, deeplearning.ai). The problem descriptions are taken straightaway from the assignments.

1. Linear models, Optimization…

The following problems appeared as a programming assignment in the Computation Photography course (CS445) at UIUC. The description of the problem is taken from the assignment itself. In this assignment, a python implementation of the problems will be described instead of matlab, as expected in the course.

The Problems

• The goal of this assignment is to implement the image quilting algorithm for
  
  texture synthesis and transfer,…

The following problem appeared as an assignment in the Algorithm Course (COS 226) at Princeton University taught by Prof. Sedgewick.  The following description of the problem is taken from the assignment itself.

The Seam Carving Problem

• Seam-carving is a content-aware image resizing technique where the image…

Long title: Measuring Semantic Relatedness using the Distance and the Shortest Common Ancestor and Outcast Detection with Wordnet Digraph in Python

The following problem appeared as an assignment in the Algorithm Course (COS 226) at Princeton University taught by Prof. Sedgewick.  The description of the problem is taken from the assignment itself. However, in the…

Poisson Image Editing

The goal of Poisson image editing is to perform seamless blending of an object or a texturefrom a source image (captured by a mask image) to a target image. We want to create a photomontage by pasting an image region onto a new background using Poisson image editing. This idea is from the P´erez et al’s SIGGRAPH 2003 paper …

In this article, some more social networking concepts will be illustrated with a few problems. The problems appeared in the programming assignments in the

coursera course Applied Social Network Analysis in Python.  The descriptions of the problems are taken from the assignments. The analysis is done using NetworkX.

The following theory is going to be used to solve the assignment problems.

 …

The following problems appeared in the programming assignments in the coursera course Applied Social Network Analysis in Python.  The descriptions of the problems are taken from the assignments. The analysis is done using NetworkX.

The following theory is going to be used to solve the assignment problems.

1. Creating and Manipulating…

The following problem appeared as an assignment in the coursera course Algorithm-I by Prof.Robert Sedgewick  from the Princeton University few years back (and also in the course cos226 offered at Princeton). The problem definition and the description is taken from the course website and lectures.  The original assignment was to be done in java, where in this article both the java and a corresponding python implementation will…

In this article, an R-hadoop (with rmr2) implementation of Distributed KMeans Clustering will be described with a sample 2-d dataset.

1. First the dataset shown below is horizontally partitioned into 4 data subsets and they are copied from local to HDFS, as shown in the following animation. The dataset chosen is small enough and it’s just for the POC purpose,…

In this article, we shall see how the Bayesian Kalman Filter can be used to predict positions of some moving particles / objects in 2D.



This article is inspired by a programming assignment from the coursera course Robotics Learning by University of Pennsylvania, where the goal was to implement a Kalman filter for ball tracking in 2D space. Some part of the problem description is taken from the assignment description.

The following…

The problem

Given a set of labeled images of  cats and dogs, a  machine learning model  is to be learnt and later it is to be used to classify a set of new images as cats or dogs. 

• The original dataset contains a huge number of images, only a few sample images are chosen (1100 labeled images for cat/dog as training…

The following problems appeared in the assignments in the Udacity course Deep Learning (by Google). The descriptions of the problems are taken from the assignments (continued from the last post).

Classifying the letters with notMNIST dataset with Deep Network

Here is how some sample images from the dataset look like:

Let’s try to get the best performance using a multi-layer model!…

• In this article, a mathematical model for the growth of a sunflower (shown below) will be described (reference: the video lectures of Prof. Jeffrey R Chesnov from Coursera Course on Fibonacci numbers).

  

• New florets are created close to center.

• Florets move radially out with constant speed as the sunflower grows.

• Each new…

1. In this assignment, some exploratory analysis is done on the criminal incident data from Seattle and San Francisco to visualize patterns and contrast and compare patterns across the two cities.
2. Data used: The real crime dataset from Summer (June-Aug) 2014 for both of two US cities Seattle and San Francisco has been used for the analysis. The datasets used for…

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Summary

In this report, the spread of the pandemic influenza A (H1N1) that had an outbreak in Kolkata, West Bengal, India, 2010 is going to be simulated. The basic epidemic SIR model will be used, it…

This shinyapp is a live shiny/R web application (hosted on shinyapps.io) that implements simple sentiment analysis POC with R, to have an insight about the people's sentiment about the smartphones from different brands released in India for a couple of weeks over a past time period, it was written a few years back (in 2014), for demonstration purpose, with the tweets…

In this article, the clustering output results using Spectral clustering (with normalized Laplacian) are going to be compared with taht obtained using KMeans clustering on a few shape datasets. 

The following couple of slides taken from the Coursera Course: Mining Massive Datasets by Stanford University 

describe the basic concepts behind…