Skills

Machine Learning and Deep Learning

Developed expertise in building and deploying machine learning models for real-world applications, such as predictive analytics, medical image classification, and sentiment analysis. Leveraged frameworks like PyTorch, TensorFlow, and Scikit-learn to implement advanced algorithms, demonstrating problem-solving skills and technical precision. These skills align with the demands of data-driven decision-making and scalable AI solutions.

Web-Based Model Deployment

Deployed machine learning models as web applications using Flask and Django, enabling user-friendly interfaces for real-time predictions. Integrated backend logic with interactive frontends, ensuring scalable and efficient deployment of machine learning solutions.

Data Analysis and Visualization

Proficient in analyzing large datasets and uncovering actionable insights using Python (Pandas, NumPy), SQL, and Excel. Created dynamic dashboards in Power BI and Tableau, enabling stakeholders to interpret trends effectively. These skills are essential for understanding business metrics and driving strategic decisions in data analyst and data science roles.

Natural Language Processing (NLP)

Gained hands-on experience in processing and analyzing unstructured text data through sentiment analysis projects. Familiarity with transformers-based models like BERT, GPT, and Hugging Face for advanced text analytics. Adds depth to your existing NLP expertise.​ Applied GloVe embeddings, Logistic Regression, and LSTM models to track real-time social media trends. This skill is crucial for extracting meaningful insights from text data in diverse industries.

Time-Series Analysis

Developed predictive models for financial forecasting and trend analysis using techniques like Fast Fourier Transform (FFT) and Gradient Boosting. This skill is pivotal for roles requiring data-driven forecasting and temporal pattern recognition in finance, healthcare, and logistics.

Programming

Skilled in Python, R, SQL, and Java, with practical experience in building end-to-end machine learning pipelines. Used Flask and Django for web-based model deployment, showcasing adaptability in integrating machine learning solutions into user-friendly platforms. Programming expertise forms the backbone of any machine learning or data analytics role.

Statistical Modeling and Feature Engineering

Applied statistical methods for data preprocessing, dimensionality reduction, and feature selection in projects like stock price forecasting and coronary artery stenosis detection. These skills ensure robust model performance and reliability, critical for machine learning engineer and data scientist positions.

Big Data and Cloud Computing

Familiar with big data technologies like Hadoop and Spark and cloud platforms like AWS, IBM Watson Studio and GCP. These skills demonstrate the ability to handle large-scale data and deploy models in distributed environments, aligning with modern data engineering and analytics practices.

Image Processing

Specialized in medical imaging projects, leveraging Swin Transformer, YOLOv5, and Histogram Equalization to enhance diagnostic precision. This skill is particularly relevant for AI applications in healthcare and related fields.

Leadership and Collaboration

Successfully led teams in competitions like Virtusa’s Jatayu Season II and academic projects, showcasing strong leadership, teamwork, and communication abilities. These soft skills complement technical expertise, aligning with roles that require cross-functional collaboration.

MLOps and CI/CD

Proficiency in tools like Docker, Kubernetes, and GitHub Actions for continuous integration and deployment of machine learning models. Aligns with roles involving scalable AI model deployment and production readiness.