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anis-kaci/README.md

Hello, I'm Anis Kaci

I'm a Data scientist intern, data engineering student, athlete and a passionate data science enthusiast with a strong focus on machine learning, especially Time series analysis on business use cases. 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  Data Science Enthusiast
  • ๐Ÿค– Machine Learning Practitioner
  • ๐Ÿ“– Lifelong Learner
  • ๐Ÿ“Š Mathematics forever curious

What I Do

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

  • Building and deploying machine learning models
  • Solving real-world business problems using data
  • Exploring the beautiful world of mathematics and its role in data science
  • Analyzing data with a statistical lens to uncover hidden patterns
  • Delving into NLP to extract insights from text data

What Im currently working on

  • Anomaly detection on time series data
  • Computer vision (OCR, text recognition..)
  • Diving into LLMs world (encoding, self attention mechanisms)
  • Coding, exploring and understanding various Neural networks concepts
  • Exploring, coding and testing ML models for different fields use cases (Business, manufacturing, healthcare..etc)
  • Probability/Statistics (Monte Carlo simulations, Estimators, stochastic processes, Markov Chains..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