Sai Keerthana Terala
B.Tech Student | ML & Computer Vision Intern • Hyderabad, India • s*****************@gmail.com • +91*******516 • linkedin.com/••••• • github.com/•••••
Professional Summary
B.Tech Information Technology student (expected July 2026) with hands-on experience in deep learning, computer vision and Python development. Completed internships in Data Science and Software Development where I built ML prototypes (classification, anomaly detection, recommendation/system analysis) and production-oriented Python applications (web scraper, algorithm implementations). Strong practical experience with TensorFlow/Keras, YOLOv8, EfficientNet, OpenCV, and model interpretability (GradCAM). Seeking internship or entry-level roles in Software Engineering, Python Development, Data Analytics, or AI/ML where I can apply model development, data pipelines, and CV expertise to deliver measurable results.
Technical Skills
Programming Languages: Python
Web Technologies: HTML,REST APIs
Frameworks and Libraries: TensorFlow,Keras,Scikit-learn,OpenCV,NumPy,Pandas,Matplotlib
Databases: SQL
Tools and Methodologies: Git,GitHub,Jupyter Notebook,Agile
Computer Vision & Detection: YOLOv8,EfficientNet-B0,GradCAM
Modeling & Core Concepts: Deep Learning,Transfer Learning,Model Evaluation,Anomaly Detection,Recommendation Systems,Sentiment Analysis
Work Experience
Infotact Solutions
Remote
Data Science & ML Intern (Virtual)
Nov 2025 – Feb 2026
Tech Stack: Python, Pandas, NumPy, scikit-learn, TensorFlow, Keras, Git, GitHub
- Designed and implemented prototype anomaly detection pipelines using Python, Pandas and scikit-learn to identify outliers in structured datasets; documented evaluation metrics and recommended feature improvements for productionization.
- Developed sentiment analysis classifiers on text datasets using TensorFlow/Keras and scikit-learn preprocessing; measured performance via confusion matrix, precision, recall and F1 to guide model selection.
- Built a proof-of-concept recommendation system combining content-based features and collaborative signals using Python and Pandas to generate ranked item suggestions for small-scale datasets.
- Performed model validation and comparative evaluation with cross-validation and holdout sets; produced clear model comparison reports to assist technical stakeholders in choosing deployment candidates.
- Collaborated with mentors in guided sessions to convert ML prototypes into deployment-ready designs, documenting input/output contracts, performance targets, and monitoring considerations.
- Managed code and experiment artifacts using Git and GitHub, maintained reproducible notebooks, and presented findings in sprint demos following Agile practices.
SkillCraft Technology
Remote
Software Development Intern (Virtual)
Oct 2025 – Nov 2025
Tech Stack: Python, Git, GitHub, NumPy, Pandas
- Developed four Python applications (Temperature Converter, Number Guessing Game, Sudoku Solver using backtracking, and a Web Scraper) demonstrating end-to-end design, implementation and testing.
- Implemented a Sudoku solver using an optimized backtracking algorithm in Python, reducing search time with heuristic ordering and validating correctness across multiple puzzle sets.
- Built a resilient web scraper to extract structured product data from 100+ e-commerce pages, implementing pagination handling, retry logic and error recovery to maximize data completeness.
- Packaged scraper outputs into clean CSV/JSON datasets suitable for downstream analysis and model training; documented data schema and preprocessing steps for reuse.
- Managed the project codebase with Git, participated in Agile sprint cycles, and used PR-based reviews to maintain code quality; received a Letter of Recommendation upon internship completion.
- Prepared developer documentation and delivered sprint demos to mentors, translating user stories into deliverable tasks and iterating on feedback to improve application robustness.
Projects
Brain Tumor Classification using EfficientNet-B0 & GradCAM
Tools Used: Python, TensorFlow, Keras, EfficientNet-B0, OpenCV, GradCAM, NumPy
- Built a deep learning classifier to categorize brain MRI scans into four classes (Glioma, Meningioma, Pituitary, No Tumor) using EfficientNet-B0 and transfer learning on a dataset of 7,023 images.
- Achieved 97% test accuracy and evaluated model performance with confusion matrix, precision, recall and F1-score; integrated GradCAM to produce interpretable heatmaps highlighting model attention over tumor regions.
Vehicle Detection & Speed Estimation using YOLOv8
Tools Used: Python, YOLOv8, OpenCV, NumPy
- Developed a real-time CCTV surveillance pipeline to detect five vehicle classes using pretrained YOLOv8 (COCO weights) and implemented centroid-based multi-object tracking with unique ID assignment.
- Implemented virtual line-crossing logic and timestamp-delta based speed estimation achieving 90.91% estimation accuracy with a 3.27 km/h mean absolute error, providing a sensor-free alternative to radar systems.
Education
Gokaraju Lailavathi Engineering College, Osmania University
B.Tech – Information Technology • Hyderabad, India • 2022 – Expected July 2026
Certifications
Google AI Essentials — Coursera
Python Essentials 1 — Cisco Networking Academy
SQL Advanced — HackerRank
Software Engineer Intern Certificate — HackerRank
Python Programming — Infosys Springboard
Achievements
- Adobe India Hackathon — Qualified for Round 2 — 2024
- IndustrAI 24-Hour Hackathon — Participant: IIT Madras
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