About
Aspiring Machine Learning Engineer and Data Scientist with a strong foundation in AI/ML, NLP, and Computer Vision, currently pursuing a B.Tech in Data Science and Artificial Intelligence. Proven ability to develop and optimize advanced models, as demonstrated by a 15% enhancement in video completion rates through hybrid recommendation engines and a 93.4% accuracy rate in schema-aware NLP agents. Eager to leverage expertise in data analysis, model deployment, and performance optimization to drive impactful solutions in a dynamic tech environment.
Work
Bengaluru, Karnataka, India
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Summary
Developed and optimized recommendation systems and data analysis pipelines to enhance user engagement and drive key metrics for a leading ed-tech platform.
Highlights
Developed a hybrid autoplay recommendation engine, integrating collaborative filtering with content-based video embeddings, achieving a 15% enhancement in video completion rates.
Performed rigorous Data Wrangling and Analysis, directly contributing to a 15% reduction in bounce rate through actionable insights.
Executed A/B experimentation, resulting in a 20% increase in session duration by optimizing user experience.
Evaluated diverse machine learning approaches, including matrix factorization, similarity scoring, and embedding optimization, to identify the most effective algorithms for improved system performance.
Skills
Languages
Python, C++(Basic), SQL.
Machine Learning
Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Recommender Systems, Ensemble Methods, Computer Vision, Image Processing.
ML Tools/Libraries
Pandas, NumPy, Feature Engineering, Data Cleaning, Matplotlib, OpenCV, PyTorch, Keras, TensorFlow, CNNs, GANs, Embeddings, YOLO, FAISS, Gym, XGBoost, ResNet.
NLP & LLMs
Transformers, Prompt Engineering, LangChain, GPT-4, T5, Fine-tuning, LoRA, NLTK, Gemini, Claude.
MLOps
Deployment, Streamlit, Flask, Docker, MongoDB, Vercel.
Cloud Services
GCP, Google Analytics, BigQuery, Vertex AI, Amazon S3.