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Python, Machine Learning, NumPy, Pandas, Classification, Data Science

Location:
Arlington, Texas, United States
Posted:
November 19, 2018

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Resume:

Y A T H A R T H G A R G

ac7qs8@r.postjobfree.com

linkedin.com/in/yatharth1908

github.com/yatharth1908

+1-682-***-****

O B J E C T I V E

Looking for an opportunity in Data Science/Machine Learning at a company which will help me whet my skills and utilize my knowledge which will help both my career and the company.

E D U C A T I O N

University of Texas at Arlington

Master of Science in Computer Science specializing in Data Science and Artificial Intelligence, Dec 2018

GPA: 3.67/4.0

The Northcap University

Bachelor of Technology in Computer Science minor in Data Science, May 2016

C O U R S E W O R K

Data Analysis and Modelling Techniques

Human Computer Interaction

Algorithms and Data Structures

Probability and Statistics

Data Mining

Software Engineering

Machine Learning

Reinforcement Learning

Multiagent Systems

Cloud Computing

S K I L L S

C/C++ Classification/Regression

Python Neural Networks

NumPy/Pandas Machine Learning

Matplotlib SQL

Keras Apache Spark

Python Flask PySpark/MLlib

Scikit-learn Amazon Web Services

MATLAB Microsoft Azure

Jupyter Notebook d3.js

P R O J E C T S

Speech Accent Recognition

Created a speech-accent recognition application using Convolution Neural Network on Python which used a dataset comprising of accents of people from 177 countries speaking a given dialogue and successfully predict their background. Accuracy achieved was around 80%.

Face Detection

Implemented Support Vector Machine on AT&T ‘Database of Faces’ by using one vs rest approach. The database contained 10 images of 40 individuals and the model returned the maximum outcome when classifications ran on testing sample using trained SVM. The accuracy achieved was 78.5%.

WoLF (Win or Learn Fast) Stochastic Game

Implemented WoLF algorithm for reinforcement learning on stochastic game which involved making the agent learn faster while losing by varying learning rates to support convergence. Winning of an agent was determined by comparing current expected payoff to average policy overtime.

Edibility of Mushrooms

Using the mushroom dataset from UCI library, implemented the Naïve Bayes classifier using Python to classify if the mushroom is edible or poisonous. The accuracy achieved was 79.85%.

Photo Sharing App

An AWS application which allowed the user to upload their image and allow other users to comment and rate your photo based on their liking. The application was created using Python Flask.

R E S E A R C H

• Published paper on Sentiment Analysis and Opinion Mining: A review of various applications in multi varied domains in an international Google Scholar journal.

• Extracted data from different social networks like Facebook, Instagram and also from websites like Amazon to create a bag of words model

• Used different machine learning and semantic orientations for researching and analyzing the data



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