An AI Enthusiast who aspires to be a Data Scientist
Programmer having a solid foundation in Python Programming and its various frameworks including Numpy, Pandas, Flask
Having Deep understanding of Supervised and Unsupervised Machine learning algorithms and experience in building,evaluating and tuning it
Proficient in implementing and optimizing various Deep Learning architectures, including CNNs,RNNs, and GANs for diverse applications.
Proficient in developing and fine-tuning NLP models for tasks such as text classification, named entity recognition, and language translation
Proficient in computer vision methodologies including feature extraction,object detection, image classification and segmentation tasks
Experienced in implementing end-to-end Machine Learning pipelines for seamless model development, deployment, and monitoring.
Developed a customer propensity model to predict purchase likelihood based on historical data and RFM Modelling to aid marketing teams of e-commerce startups. Developed a Flask application to provide an interface to get predictions, implemented CI/CD pipelines with GitHub Actions for automated testing, building, and deployment.
Built a product recommender that recommends a product using the items added in the cart through association rules mined from the sales transaction data to aid customers and marketing teams. Developed a flask application to provide an interface for recommender, built CI/CD pipelines with GitHub Actions for automated testing, building, and deployment
Built a forecasting system to predict daily patient length of stay for optimizing costs and improving care delivery. Implemented a training pipeline to pull new patient data, perform batch predictions, and log results into the database.Developed a retraining pipeline to check for data/model drift and redeploy the model as needed.
Built an Aspect-Based Sentiment Analyzer for restaurant reviews by implementing GRUs with custom attention mechanism using tensorflow. Given a review and aspect, this model predicts sentimentof the reviewer about that aspect. Developed an interface for the translator using Streamlit and deployed it on the streamlit cloud.
Built a language translator by implementing a custom Transformer model with Multi-head self attention from scratch using tensorflow and trained it with hinglish2english dataset. This model translates Hinglish (Hindi written in english alphabets) sentences to English, uploaded the trained model on hugging face hub
Developed a search engine to retrieve news articles relevant to the user query using ANNOY algorithm and SBERT model embeddings. Implemented the entire pipeline, from data ingestion to embedding generation and ANNOY indexing. Deployed the dockerized solution on AWS EC2 instance with CI/CD Pipelines using GitHub Actions.
Implemented various Machine learning algorithms, metric and functionalities of sklearn using python from scratch which demonstrates my ability to code things from scratch
Implemented various Convolutional Neural Networks architectures like AlexNet, LeNet, InceptionNet and ResNet50 using Pytorch from scratch demonstrating my ability to implement convnets
Coded Neural style transfer algorithm to style images from scratch using pytorch. Used VGG19 model pre-trained on ImageNet to extract features from both content and style images
Implemented Markov chains for computing the transition probabilities between different marketing channels in user's journey aiding marketing teams to attribute channels appropriately
Gather sales and transactions data from a burger cafe.Price optimization by performing data analysis and regression technique to find the optimal prices for food products to achieve maximum profit
Acquired proficiency in web development,creating applications and RESTful APIs with Flask. Also, gained experience in database interaction
Master advanced Python data analysis and manipulating complex datasets, extract insights,
Completed and obtained course completion certificate through an offline proctored examination with a top performance with 84 %
Gained a solid understanding of the fundamental AWS concepts and services such as EC2, S3, RDS, and Lambda.
Acquired knowledge and gained hands-on experience of AWS S3 to store and retrieve data using S3 buckets
Learnt the fundamentals of data analysis using python. Gained hands on in analyzing large datasets
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