Computer Vision

Photo by timJ on Unsplash

Object detection is an important part of image processing. The autonomous vehicle has to detect lanes, road surfaces, other vehicles, people, signs, and signals, etc. We live in such a dynamic world, and everything is constantly changing. While ago, a friend gave me a PDF file, told me to locate and get a specific value in PDFs. The application of Object Detection is everywhere. Here is another example, Data scientists are using Object Detection to identify the planet disease from the vegetable leaf.

A few weeks ago, I was doing research on Deep Learning and Computer Vision on autonomous cars…

Photo by NASA on Unsplash

Around 2010, I remember rotating image was popular among adults. Simple tasks like resizing, affine, rotate, transformation has been used whether it’s a photo album from the old days or road analysis by electronic vehicle. A few weeks ago, I had the opportunity to read articles about self-driving cars and image processing.

In the good old days of high school math, we all have done matrix transformation. While thinking about this topic, I find this as the closest one to define what image Transformation is. Image Transformation is similar to matrix transformation. After all, behind every image, there is a…

Deep Learning


This weekend, I ran into an interesting topic. The topic is deep-fakes. There have been many reports about fake videos of popular celebrities or politicians. These manipulated videos are created by manipulating original videos. These fake videos are hard to detect from the naked eyes, and they are becoming an important problem in society.

Social media platforms like Facebook, Twitter, Youtube, etc. are the main distribution channel for these manipulated videos. While these platforms are working on solving this problem, Facebook is making a big investment ($10 million) to tackle this problem, and other platforms like Twitter and Google are…

Machine Learning, Programming

In this article, I am going to share a few machine learning work I have done in spark using PySpark.

Machine Learning is one of the hot application of artificial intelligence (AI). AI is a much bigger ecosystem with many amazing applications. Machine learning in simple terms is the ability to automatically learn by the machine and improve from experience without explicitly programmed. The learning process starts with observation of data, then it finds the pattern in date and makes a better decision on learning from data.

Categories of Machine Learning Algorithm

There are usually supervised and unsupervised learning but some of the fell in…

Data Science, Programming

photo credit: Octávio Deluchi

There are many tutorials about pandas on the internet and books. Pandas library is one of the most used libraries by data scientists and data engineers. In this tutorial, I am going to list the pandas’ functions I use the most.

While there are many functions to select here are my top ten most used ones. Please let me know yours in the comment section. I will be happy to add them to my collections. I will be focusing on pandas Data Frame rather than series.

Let’s starts with importing pandas.

import pandas as pd

1. Files Operation


import pandas as pd…


Analysis using Family Members and Friend relationship similar to LinkedIn

We are exploring the family member relationship with each other and friends using GraphX. There are six family members with father, mother, son, daughter, and two friends. We are trying to answer which family member has to most connection and which family member has the least connection. Also, we can see the level of relationship between records in the dataset. These are the questions solve by GraphX and has been largely used by companies like Facebook and LinkedIn.

GraphX Introduction

GraphX is Apache Spark’s API for graphs and graph-parallel computation. GraphX unifies ETL (Extract, Transform & Load) process, exploratory analysis, and iterative…

neupane parlad

Data Science Professional, Experience Java Developer, Check out my new article published on Analytics Vidhya:

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