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I am a Microsoft Certified Data Scientist with working experience in data analytics including data acquisition, data warehousing, real-time dashboarding, machine learning, and many more. Hands-on experience leveraging cloud-based data science technology stack to solve challenging business problems. I'm also open to having a chat about Anything Data Science Related.

Want to know more about me? Check out my portfolio

I have technical experience and interest in :

  • Business Intelligence
  • Machine Learning
  • Cloud Computing
  • Natural Language Processing
  • Computer vision

Current Stack

Languages and Tools:

bash

bootstrap css3 docker flask git heroku html5 linux mongodb mysql opencv postman python pytorch scikit_learn tensorflow

Current Stats

Hasanain's github activity graph
Hasanain's GitHub stats Hasanain's GitHub Streak
Top Langs

I like to experiment with cutting-edge tech during my free time 👨🏻‍💻

Orignal Fake

Hasanain Mehmood's Projects

iot-device-identification icon iot-device-identification

This project contains the production ready Machine Learning(Deep Learning) solution for detecting and classifying an IoT device connecting the network

nlp-hub icon nlp-hub

The NLP Hub consists of multiple NLP services, each providing specific functionality, such as sentiment analysis, named entity recognition, grammar & spell check, and text summarization. The system is designed to handle a high volume of text data.

oneflow icon oneflow

It is a Package having the Implementation of ANN with callbacks

onespace icon onespace

A high-level python Framework with low-code support for Machine and Deep Learning

recommendersystems icon recommendersystems

This Project provides the end-to-end implementation of multiple Recommender Systems

sensor-fault-detection icon sensor-fault-detection

In this project, the system in focus is the Air Pressure system (APS) which generates pressurized air that are utilized in various functions in a truck, such as braking and gear changes. The datasets positive class corresponds to component failures for a specific component of the APS system.

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