Machine Learning Data Science

Tadikamalla Gowtham Krishna

College Park, MD ****2 240-***-**** *********************@*****.*** linkedin.com/in/Gowtham-Krishna Education

University of Maryland College Park, College Park,MD Expected May 2026 Master of Data Science

Coursework: Statistical methods in Data Science, Automated Learning and Data Analysis, Big Data Systems, Design and Analysis of Algorithms.

Amrita School of Engineering,Coimbatore, Tamil Nadu, India May 2024 Bachelor of Technology in Electrical and Computers, GPA: 8.16/10 Coursework: Machine Learning, Artificial Intelligence, Natural Language Processing, Applied Analytics, Cloud Computing, Computer Networks, Databases, Operating Systems, Computer Architecture, Digital Image Processing. Skills

Languages: Python, C, C++, Java, JavaScript, R Programming, MATLAB, Assembly, Embedded C Machine Learning Tools: Pandas, Data science, Clustering, NLP, TensorFlow, PyTorch, Huggingface, Langchain MLOps & Data Engineering: Jenkins, Docker, Kubernetes, Git CI/CD Cloud: Lambda, S3, Step Functions, DeepRacer

Databases and Operating Systems: MySQL, DynamoDB, Oracle, Windows, Ubuntu Office Ware: Excel for data analysis, Outlook, PowerPoint, Word, Power BI, Canva Certifications: Intel Edge AI Certification, AWS Machine Learning Foundations Experience

Testing Intern, ICU Medical LLP, Chennai, India Jan 2024 – July 2024

● Developed APIs for automation and wrote Python scripts to test embedded systems in medical devices like Dual and Solo, ensuring enhanced reliability and efficiency.

● Implemented Pytest framework to conduct comprehensive functional and integration testing, reducing manual testing time by 80% and improving accuracy.

● Automated the testing process using Jenkins, optimizing the CI/CD pipeline and streamlining the defect diagnosis and resolution process for quicker product iterations. Projects

Hate Speech Detection Using Machine Learning: Developed a machine learning-based system to identify and categorize hate speech in online text, utilizing NLP techniques for data preprocessing and feature extraction. Utilized open-source datasets and data labeling tools to classify text into two categories: Hate Speech (1) and Non-Hate Speech (0). Applied various statistical significance techniques to derive actionable insights, enabling organizations to monitor and mitigate hate speech effectively. Employed and compared the performance of three models: Naive Bayes, LSTM, and DistilBERT, demonstrating that DistilBERT achieved the best performance with 94% accuracy. Delivered data-driven recommendations to improve content moderation policies and foster a safer online environment.

Plant Health Monitoring System Using Deep Learning: Developed a deep learning-based system for monitoring the health of tomato plants, utilizing manual data collection and data labeling tools to annotate images into three classes: Early Blight, Healthy, and Magnesium Deficiency. Achieved 92% accuracy in disease classification using the YOLOv8 model. Designed and integrated an Innovative rail system for real-time video capture, enabling live disease detection through a user-friendly website interface. Provided actionable insights for farmers to improve crop management and reduce disease impact. Credit Score Classification: Developed a machine learning model for credit score classification into three categories: High, Medium, and Low, using K-NN, PCA, SVM, and Neural Networks. Utilized PCA for dimensionality reduction and optimized classification accuracy by applying multiple algorithms. Achieved high accuracy, offering valuable insights for financial institutions to assess risk management and make data-driven decisions. Python File Tagger: Created an Open-source Python software integrated with MySQL for efficient file organization and retrieval. Designed a user-friendly tag-based system enabling users to categorize and locate files based on content or purpose. Streamlined the file management process, enhancing productivity and efficiency for users handling large volumes of files. Provided an intuitive solution for quick file access and organization in complex data environments. Extracurriculars

Technical Lead at Intel IOT Club

● Managed a team of 30 tech enthusiasts while coordinating over 500 members, successfully organizing numerous AI and IoT events that fostered knowledge sharing and collaboration..

● Conducted and organized multiple hackathons and events, delivering IoT and edge AI-focused training for students using Intel® Software Development Tools and resources, significantly enhancing technical skills and promoting innovation within the community while demonstrating strong leadership and commitment to the growth of the IoT club.

Contact this candidate