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anis-kaci/README.md

Hello, I'm Anis Kaci

I'm a Junior Data scientist, data engineering student, athlete and a passionate data science enthusiast with a strong focus on Large Language Models, I also have a deep interest in mathematical concepts, particularly those closely related to data science, such as probability and statistics.

About Me

  • ๐Ÿ“š Data Engineering Student
  • ๐Ÿง  LLM Enthusiast
  • ๐Ÿค– GenAI Practitioner
  • ๐Ÿ“– Lifelong Learner
  • ๐Ÿ“Š Mathematics forever curious

What I Do

I'm constantly exploring the fascinating world of data science and AI. My work involves:

  • Large Language Models Theory (Architectures,BERT,GPT...etc)
  • Building GenAI applications using LLMs
  • Solving real-world business problems using data
  • Exploring the beautiful world of mathematics and its role in data science

What Im currently working on

  • Fine tuning LLM models for different purposes, using different techniques (DeepSeek, LoRA, QLoRA)
  • GenAI for sentiment analysis (Speech-to-text, text-to-speech)
  • Keep up with LLM most recent architectures and trends (DeepSeek, Agents..)
  • Coding, exploring and understanding various Neural networks concepts
  • Exploring, coding and testing ML and deep learning models for different fields use cases (Business, manufacturing, healthcare..etc)

Let's Connect

I'm always excited to connect with fellow data enthusiasts, researchers, and anyone who shares a passion for data science. Feel free to reach out and let's learn and grow together.

LinkedIn

Check Out My Work

Explore my repositories to see some of the projects I've been working on. From machine learning experiments to , there's a bit of everything.

Thanks for Visiting

Thank you for checking out my GitHub profile.

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  1. Breast-cancer-random-forest-with-docker-deployment Breast-cancer-random-forest-with-docker-deployment Public

    Breast cancer classification using the random forest model and deployment with docker

    Python

  2. Isolation-Forest-anomaly-detection Isolation-Forest-anomaly-detection Public

    Anomaly/outlier detection using Isolation forest

    Jupyter Notebook