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Tejeshyewale/README.md

Tejesh S. Yewale

Machine Learning Engineer β€’ Data Science β€’ Cloud & MLOps


🧠 About Me

I am a Computer Engineering student focused on building end-to-end Machine Learning systems with strong foundations in:

  • πŸ€– Machine Learning & Deep Learning
  • ☁ Cloud Deployment (AWS)
  • βš™ CI/CD & Docker
  • πŸ“Š Data Analytics & Visualization

My goal is to become a Production-Level ML Engineer at a top MNC, building scalable AI systems.


πŸš€ Featured Work

Project Description Stack
πŸ” Chicken Disease Classification End-to-End Deep Learning Pipeline TensorFlow, Flask, Docker
πŸ“° Fake News Detection NLP Model with Cloud Deployment Sklearn, AWS
🍽 Restaurant Rating API ML Model Deployed on AWS SageMaker, Lambda
πŸ›’ Supermarket Sales Dashboard Business Intelligence Insights Power BI

πŸ›  Technical Expertise

Programming

Python SQL C++

Machine Learning

Scikit-Learn TensorFlow Pandas

Cloud & DevOps

AWS Docker GitHub Actions

Analytics

Power BI Excel


πŸ“Š GitHub Analytics

πŸ“Š GitHub Stats

πŸ† Highlights

  • πŸ… AWS Cloud Essentials Certified
  • πŸ‘¨β€πŸ’Ό Data Science Lead – Linux Club
  • πŸš€ Built Multiple End-to-End ML Systems
  • 🌱 Active Open Source Contributor

🌐 Connect


Building scalable AI systems with clean engineering principles.

Popular repositories Loading

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    Welcome to the Hacktoberfest 2024 repository! This repository is dedicated to encouraging contributions during the month of October as part of the annual Hacktoberfest celebration

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    Build a serverless ETL pipeline using AWS Glue to process and analyze CSV data stored in S3 with Athena.

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    An end-to-end Loan Eligibility Prediction System built with FastAPI, Machine Learning, JWT Authentication, and a Streamlit UI. The application allows users to securely sign up, log in, submit finan…

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  5. feature-engineering-guide feature-engineering-guide Public

    A beginner-friendly and interview-ready guide to Feature Engineering in Machine Learning, covering missing data handling, encoding, scaling, and feature creation with clear explanations and examples.

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