Sree Chakri
Career Objective
Machine Learning Engineer with 0 years of experience applying ML and DL to computer vision and NLP tasks; built and deployed models for road-surface irregularity classification (94% accuracy) and sentiment analysis (92% with LSTM). Strong foundation in Python, data science libraries, and cloud fundamentals; seeking an entry-level ML/Data Science role to apply model development and deployment skills.
Education
Technical Skills
Projects
- Built and deployed an ML/DL pipeline to detect and classify road surface irregularities (e.g., potholes, cracks) from image and sensor data, improving detection precision for autonomous navigation.
- Designed and trained convolutional neural network models using TensorFlow; optimized architecture and augmentation leading to >94% classification accuracy on the test set.
- Engineered preprocessing and feature pipelines in Python with NumPy and Pandas to standardize image inputs and reduce noise, improving model robustness in varied lighting and surface conditions.
- Implemented evaluation and validation procedures (confusion matrices, precision/recall by class) to identify failure modes and iterate on model improvements.
- Packaged model and inference code for deployment using lightweight serving scripts, enabling reproducible inference on validation data and demonstration pipelines.
- Performed end-to-end sentiment analysis on Amazon food reviews to extract customer sentiment trends and actionable insights for product improvement.
- Implemented text preprocessing and feature extraction pipelines (tokenization, stop-word removal, vectorization) using NLTK and Pandas to prepare data for modeling.
- Developed and compared classical and deep models: Naive Bayes (scikit-learn) yielded ~87% accuracy; implemented LSTM-based sequence model for improved contextual understanding reaching ~92% accuracy.
- Quantified sentiment distribution and derived business insights showing ~85% positive and 15% negative feedback; translated findings into recommendations for product and UX improvements.
- Validated model performance using cross-validation and confusion matrix analysis to identify bias toward majority classes and applied class-weighting where needed.
Certifications
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