TECHNICAL EXPERTISE

Skills & Technologies

A comprehensive overview of my technical skills, frameworks, and tools. Continuously learning and adapting to the latest advancements in AI and machine learning.

Programming Languages

Proficiency levels based on project complexity and practical application

Python Advanced
R Intermediate
SQL Intermediate
JavaScript / TypeScript Basic
Bash / Shell Scripting Intermediate

Machine Learning & Deep Learning Frameworks

Expertise in industry-leading ML/DL frameworks and libraries

TensorFlow & Keras Advanced
PyTorch Advanced
scikit-learn Expert
XGBoost / LightGBM Advanced
Hugging Face Transformers Intermediate

Data Science & Analytics Tools

Comprehensive toolkit for data manipulation, analysis, and visualization

Pandas & NumPy Expert
Matplotlib & Seaborn Advanced
Plotly & Dash Intermediate
Jupyter Notebooks / JupyterLab Expert
Apache Spark (PySpark) Basic

Specialized Libraries & Tools

Domain-specific libraries for computer vision, NLP, and bioinformatics

OpenCV (Computer Vision) Advanced
NLTK & spaCy (NLP) Intermediate
Biopython (Bioinformatics) Advanced
LangChain (LLM Applications) Intermediate
OpenAI API & RAG Systems Intermediate

Cloud Platforms & MLOps

Experience deploying and managing ML models in production

Google Cloud Platform (GCP) Intermediate
Vertex AI Intermediate
Docker & Containerization Intermediate
Git & GitHub Advanced
CI/CD Pipelines Basic
AWS (EC2, S3, Lambda) Basic

Domain Expertise

Specialized knowledge in healthcare AI and bioinformatics

🏥

Healthcare AI

  • Medical imaging analysis (MRI, CT, X-ray)
  • Clinical data prediction models
  • DICOM image processing
  • Healthcare data privacy (HIPAA compliance)
🧬

Bioinformatics

  • Protein sequence analysis
  • Genomic data processing
  • Biological database queries
  • Computational biology workflows
👁️

Computer Vision

  • Image classification & segmentation
  • Object detection & tracking
  • Feature extraction & engineering
  • Data augmentation techniques
💬

Natural Language Processing

  • Text classification & sentiment analysis
  • Named entity recognition
  • Transformer models & fine-tuning
  • LLM integration & prompt engineering
📊

Machine Learning

  • Supervised & unsupervised learning
  • Feature engineering & selection
  • Hyperparameter tuning
  • Model evaluation & validation
🔬

Research Methodology

  • Experimental design & analysis
  • Statistical hypothesis testing
  • Scientific writing & publication
  • Literature review & implementation

Professional Skills

Essential skills for effective collaboration and project success

Continuous Learning

I'm committed to staying current with the latest developments in AI and machine learning. Here's what I'm currently learning:

Currently Exploring

  • Large Language Models (LLMs): Advanced prompt engineering, fine-tuning techniques, and building RAG systems
  • Reinforcement Learning: Deep RL algorithms and applications in robotics and game AI
  • MLOps Best Practices: Advanced deployment strategies, model monitoring, and A/B testing
  • Federated Learning: Privacy-preserving ML for healthcare applications
  • Explainable AI (XAI): Techniques for interpretable ML in critical applications

Learning Resources I Use

Coursera Papers with Code ArXiv Fast.ai DeepLearning.AI Kaggle YouTube Lectures Technical Blogs

Let's Build Something Amazing

Interested in my skills? Let's discuss how I can contribute to your team and projects.