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Machine Learning EngineerJan 2018
GIS with AI. Extracting building footprints from satellites imagery using state of art deep learning architecture and advanced computer vision techniques. Skills Used: Image enhancement, Feature Extraction, Deep Learning , Remote Sensing, Computer Vision, Pytorch, OpenCV, GDAL
Software EngineerJun 2017 - Jan 2018 (7 months)
Context-sensitive spelling correction method for detecting and correcting non-word errors in digital text documents. The approach hinges around data statistics from Google Web NGrams data. It encompasses an error detector, candidate spelling generators and context sensitive error corrector. Automatic Speech Recognition System | Deep Learning Implemented Phoneme Classification, Fricatives and Stop Words Classification in audio files using Deep Learning LSTM and MLP architecture.
Major Project Brain Computer InterfaceFeb 2017 - May 2017 (3 months)
Worked on an idea to develop the system that will be operated using the Brain [login to view URL] a prototype of wheelchair that is operated by Arduino. OpenBCI Cyton Board was used to obtain the signals reflecting the user’s brain activity. Unsupervised K-Means Algorithms was applied on Fast Fourier Transformed data of EEG Data signal to make system autonomous. This system can be used by physically disabled and paralysed human to do daily life activities using their brain signals.
Project - Humanoid RobotOct 2016 - May 2017 (7 months)
Prithvi is Humanoid Copycat Robot which copies the actions of the human using Kinect Sensor. Apart from this Raspberry Pi computer is used that controls motion and function of Robot. My task is Designing Motion and Balancing of Robot using different sensor's data generated during the training phase. Apart from this,
InternshipJun 2016 - Aug 2016 (2 months)
As a TCS Intern developed a web application "Hospital Appointment System".This application helps the user to book appointment at his or her remote location without being present at the hospital. This application provides lots of functionalities which enhance user engagement and easiness to book the appointment.
Bachelor of Technology2013 - 2017 (4 years)
Deep Learning in Python (2017)Datacamp
ANALYSIS OF TWITTER DATA WITH MACHINE LEARNING TECHNIQUES.
This paper deals with how different Machine-Learning Techniques classify on features of timewindows of Twitter, a micro-blogging social media and to determine whether or not these times-windows are followed by Buzz events. In particular, we compare different machine learning techniques like Naïve Bayes and SVM, to find the accuracy of classification with or without applying dimensional reduction in the number of attributes with the help of PCA algorithms.