Hi! My name is Ram Vegiraju and I am currently a Solutions Architect at Amazon. I am passionate about understanding the theory behind Machine/Deep Learning algorithms and implementing full-stack Machine Learning solutions at scale for interesting problems in fields such as NLP and Computer Vision. In my free time I love writing, reading, and playing basketball or tennis. Read my latest article here. Feel free to check out my Resume/Skills below and some of my projects and hobbies in the other sections!
B.A. Degree in Statistics, Minor in Computer Science• Sept 2017 - Dec 2020
Relevant Coursework: Machine Learning, Linear Algebra, Software Development Methods Java, Data Analysis with Python, Data Science with R, Mathematical Statistics & Probability, Regression Analysis
R&D Software Engineering Intern • June 2020 - August 2020
Worked on building and presenting prototypes as an R&D Engineer over 12 weeks for two different customers.
Data Science Intern • June 2019 - August 2019
Worked on the backend for the Targeted Agent and Profiling Utilization Registry (TAPUR) team.
I have experience with implementing various Supervised and Unsupervised models such as MLR, Logistic Regression, k-Means Clustering, PCA, CART/Random Forest, and SVM. In addition, I am well versed with deep learning architectures such as ANN's, CNN's, and RNN/LSTM's as well as their application in fields such as NLP and Time-Series. Currently learning more about GANs and Reinforcement Learning.
A music-themed web application that takes in user features such as favorite genre/mood, artists, language, and playlist size to curate a playlist. Scraped data using Spotify's Developer API, currently incorporating more languages for users to input. Developed a K-Nearest Neighbors based Recommender System for song suggestions, used Flask for ML model inference on backend, with HTML/CSS/JS on the frontend for the user form.Skills/Tools: Python, Flask, HTML/CSS/JS, Sci-Kit Learn
Developed a web application that takes in text of any length and returns a summary with the entities extracted and highlighted. Preprocessed and cleansed data for analysis with NLTK and used Spacy for the Entity Extraction. Built web application with Flask on backend, HTML/CSS on the frontend.Skills/Tools: Python, Flask, NLTK, Spacy, HTML/CSS
Developed a simple web application that takes in an image and returns a black & white sketch using the OpenCV library.Skills/Tools: Python, OpenCV, HTML/CSS
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