Abhik Sarkar

I am a Data Science Professional having Computer Science background and interest in Machine Learning, Deep Learning, and Data Visualization. I am a continuous learner who is looking forward to kickstarting my career in Data Science Domain.


Experience

Machine Learning Engineer

Quantiphi Inc.

working as a Machine Learning Engineer for AthenasOwl which is a Machine Learning powered video recognition product of Quantiphi. It mines granular details in a video to identify faces, detect objects, identify locales, recognize spoken keywords, and much more. My Day to Day work generally revolves around CNNs/ Image Aspect of Machine Learning.

Apr 2019 - Present

Business Technology Ananlyst

Deloitte Consulting (Offices of the US)

Bring to the table win-win survival strategies to ensure proactive domination. At the end of the day, going forward, a new normal that has evolved from generation X is on the runway heading towards a streamlined cloud solution. User generated content in real-time will have multiple touchpoints for offshoring.

June 2018 - March 2019

Machine Learning Intern

Techment Technology

Capitalize on low hanging fruit to identify a ballpark value added activity to beta test. Override the digital divide with additional clickthroughs from DevOps. Nanotechnology immersion along the information highway will close the loop on focusing solely on the bottom line.

May 2017 - July 2017

Education

Udacity

Nanodegree
Data Analyst
October 2018 - February 2019

National Institute of Technology, Raipur

Bachelor of Technology
Computer Science & Engineering
August 2014 - May 2018

Skills

Machine Learning 80%

Statistics70%

Data Analytics85%

Deep Learning60%

Python70%

Programming Languages & Tools


Projects

Label faces in Group photos using Facenet

Computer Vision

Created Image Embeddings using FaceNet algorithm, clustered those images Embeddings using Hierarchical Clustering. Labeling the images with the help of clusters and running the classifier in the labelled images to generate a SVM classifier. Using the classifier Identify the Faces in the Group Photo and put a label on them using bounding Box. The detection part of the project is hosted Online.

Detecting Diabetic Retinopathy With Deep Learning

Computer Vision

The major objective of this project was to early identify the effect of Diabetes in early stages, we were given a 5 level of severity of the Disease corresponding to the 5-Classes which is to be predicted. Got a Score of 0.74 which corresponds to the top 30 kaggle score.


Awards & Certifications

  • Won Backyard Data Scientist Competition organized by IIIT-Naya Raipur
  • 1st Place - National Institute of Technology - Techincal Model Building Competition 2017
  • Represented as a Team Leader in Smart India Hackathon,2018 Organized by Govt of India.
  • Completed Python Track of DataCamp which is comprised of 20 individual Courses.