Python, Machine Learning, OpenCv, Tensorflow, SQL, Tableau
Resume:
YERUVA VENKATA SURENDER
Machine Learning Engineer
Experience
Profile
My objective is to work in an
organization where my knowledge
will help the organization to grow in
all aspects and I can acquire new
Knowledge and sharpen my skills that
helps to achieve organizational goals.
Contact Details
Mobile:
Email Id:
********.****@*****.***
LinkedIn:
www.linkedin.com/in/surender-
April 2018 -
April 2019
Medintu Health Solutions Pvt Ltd
Work with the team in generating training data, feature selection, system implementation and evaluation
Work with Machine Learning Algorithms to develop technique for Object Detection
Experience in developing and applying machine learning methodology for solving complex applied problems
Knowledge on developing models and debugging in C and Python.
Knowledge of open-source Machine Learning Framework
Research and work experience in Opencv
reddy-1137618a/
Skype ID: surender y.v
DOB: 19-Aug-1994
Education
M.Tech from Vellore Institute
of Technology (VIT - Chennai
Campus) in Computer
Science and Engineering with
7.44 CGPA.
B.Tech in Electronics and
Communication from Vidya
Vikas Institute of
Technology(Chevella) with
64.14%
Intermediate from Sri
Chaitanya Junior College
S.S.C from Sri Vijaya Sai High
School (Bodhan) with
79.00%.
Project Title: Fraud Detection Using Opencv
Description:
Identity Card fraud detection is the main objective of the project.
In this as a team member my role is to recognize the outliers and requirements.
Working on the algorithms which are suitable for the project.
Working with the yolo-dataset to train the algorithm.
And after the training dataset, check whether it is working properly or not.
Detection of ID card images with real time faces.
Knowledge on developing various machine learning and face recognition algorithms.
Project Title: Brand Analysis Using Opencv
Description:
Experience in web scraping using python for the images from web and videos.
As a part of team, mainly worked with the brands and annotating of the images.
Using Opencv converting video into images as the 25 frames per second.
Split the images dataset for training and test data for the model.
Preparing the Brand Representation document for the brands to define class for each brand.
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