Yaradesi Sai Kumar
Aspiring Machine Learning Engineer • Guntur, India • s********@gmail.com • +91*******131 • yaradesi-sai-kumar/••••• • drivetube.ai/•••••
Professional Summary
Machine Learning Engineer Intern with 1+ years of experience applying machine learning and deep learning to biomedical signal and tabular datasets. Experienced in data preprocessing, EDA, model development (classical and transformer-based), and reproducible experimentation using Python, scikit-learn and PyTorch. Seeking internship/entry-level roles to apply signal-processing and deep-learning skills to deliver robust, production-ready models.
Technical Skills
Programming Languages: Python,Java,C
Frameworks and Libraries: scikit-learn,PyTorch,NumPy,Pandas,SciPy,Matplotlib,Seaborn
Databases: SQL,DBMS
Data and Analytics: Supervised Learning,Feature Engineering,Model Selection,Hyperparameter Tuning,Transformer Architectures,Data Cleaning,Exploratory Data Analysis,Jupyter Notebooks
Tools and Methodologies: Git,GitHub,Joblib,pickle
Core Computer Science: Data Structures,Algorithms,Operating Systems
Work Experience
Skill Dzire
Data Science Intern
2024 – 2024
Interned at a data-science services environment, supporting end-to-end data preparation, exploratory analysis and supervised model development for client datasets.
Tech Stack: Python, pandas, NumPy, scikit-learn, matplotlib, seaborn, Jupyter, Git, joblib, pickle
- Led data ingestion and end-to-end preprocessing for multiple client tabular datasets using pandas and NumPy, implementing robust handling for missing values, categorical encoding, and outlier treatment to prepare data for modeling.
- Performed exploratory data analysis and visualizations with matplotlib and seaborn to identify predictive features and inform feature-engineering decisions for model pipelines.
- Developed and evaluated supervised models (logistic regression, random forest) using scikit-learn; designed cross-validation workflows to compare model performance and recommend best-fit algorithms for stakeholders.
- Implemented hyperparameter tuning with GridSearchCV and scikit-learn pipelines to improve validation generalization and reduce overfitting across candidate models.
- Packaged trained models as serialized artifacts (joblib/pickle) and produced reproducible Jupyter notebooks documenting preprocessing, training, and evaluation steps for handover to engineering teams.
- Collaborated with a small team using Git for version control, produced concise technical reports and presentations summarizing model findings and data-driven recommendations for non-technical stakeholders.
Projects
Transformer AttentionNet: EEG Spectral Feature Learning | 2025 – 2025
Tools Used: Python, PyTorch, NumPy, SciPy, Matplotlib, Signal Processing
- Designed and implemented a transformer-based AttentionNet to learn spectral representations from EEG signals for brain-state classification under anesthesia.
- Executed signal preprocessing and spectral feature extraction using SciPy and NumPy, creating inputs tailored for transformer attention blocks.
- Trained and validated the model in PyTorch, implemented attention mechanisms to focus on salient spectral bands, and analyzed model behavior via visualization of attention scores and loss curves.
Education
RVR & JC College of Engineering, Guntur
B.Tech in Computer Science and Engineering (Data Science) • Guntur, India • 2022 – 2026
Sri Vasavi Jr College, Cumbum
Intermediate (MPC) • Cumbum, India • 2020 – 2022
Certifications
Problem Solving Through Programming in C — NPTEL
Psychology of Stress and Well-Being — NPTEL
Achievements
- GATE Qualified — 2026: Qualified the Graduate Aptitude Test in Engineering (GATE) exam.
- NCC 'B' Certificate with 'A' Grade — 2025: Awarded NCC 'B' certificate and achieved 'A' Grade as an NCC cadet (joined 2023).
- High Academic Performance — 2025: Achieved a CGPA of 9.49 in B.Tech (CSD) coursework.
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