pritomdas's Projects
Open source content from a book in progress, Hands-on Algorithmic Problem Solving
Approaching (Almost) Any Machine Learning Problem
Designed an automated Tinder Bot that swipes right for users with a Facebook and Tinder account. Saves 100% of user’s time through automatic scrolling. Simply add Facebook credentials and the bot does the mundane task of swiping for busy geeks.
A collection of awesome readme templates to display on your profile
A big data project that utilizes E3, Athena, EMR, SageMaker and QuickSight on AWS to build Random Forest and xgBoost model in Spark and SQL that predict the CTR of ads on a large relational database.
AWS EKS Kubernetes - Masterclass | DevOps, Microservices
Azure AKS Kubernetes Masterclass
Azure Quickstart Templates
In this project there was application of Deep Learning to detect brain tumors from MRI Scan images using Residual Network and Convoluted Neural Networks. This automatic detection of brain tumors can improve the speed and accuracy of detecting and localizing brain tumors based on MRI scans. This would drastically reduce the cost of cancer diagnosis and help in early detection of tumors without any human involvement and would essentially be a life saver. We have also compared the accuracy of results obtained by using two different models - ResNet50 and ResNet18 and used Transfer Learning to tune or freeze weights to evaluate what gives best result.There are 3929 brain MRI scans which are either positive or negative cases of brain tumor. Models were built using ResNet50 and ResNet18 and evaluated their performance in detecting positive or negative cases of brain tumors.
This contains a compilation of Cloud Computing related projects for hands-on development and practice involving AWS, DevOps, Microservices, Linux and Powershell.
This repository contains coding interviews that I have encountered in company interviews
Study Guide for CompTIA Security+ SY0 501 exam
The Data Engineering Cookbook
Cyber attack attribution is the process of attempting to trace back a piece of code or malware to a perpetrator of a cyberattack. As cyber attacks have become more prevalent, cyber attack attribution becomes more valuable. The process of cyber attack attribution can be done using reverse engineering. From the metadata of the malware executable file, we can gather data such as date of creation, variable names used, and what library calls are imported. This information can be used as features for attribution analysis. We need to extract the features from malware that can be used for attribution and analyse them using some technique to attribute the attacks.
Expose dashboard kubernetes using Cert-Manager, Nginx Controller and Oauth2 proxy
APTnotes data
A LaTeX resume template, tailored for the recent graduate who aspires to be a Data Scientist/Engineer.
This contains all the assignment solutions of the CS 6360 class that I have taken under amazing professor John Cole during the Fall 2019
Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization. DevOps Interview Questions
This contains solutions of CS 5333 taken under wonderful and one of the funniest professor, Jorge Cobbs, during the Fall 2019
Docker Fundamentals
List of Data Science Cheatsheets to rule the world
This uses Haberman's Survival Data Set from UCI Machine Learning Repository
Infrastructure, containers, and serverless apps to AWS, Azure, GCP, and Kubernetes... all deployed with Pulumi
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent