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ahtabekdas/README.md

🌟 Hi, I’m Abdullah Bekdaş, Data Science and Artificial Intelligence Enthusiast 🌟

🚀 About Me

  • I am a results-driven data scientist with a strong foundation in deep learning techniques. Passionate about solving complex problems through data-driven insights, I am always seeking opportunities to apply my skills and knowledge in a dynamic and innovative environment. My current focus areas include machine learning, deep learning, and artificial intelligence. I am committed to continuously advancing in these fields, combining my passion for data science with an eagerness for continual learning.

🔍 Interests

  • My interests span across machine learning, deep learning, artificial intelligence, data science, and cybersecurity. I am dedicated to deepening my knowledge and acquiring new skills in these areas, always remaining open to learning new things and adopting a results-oriented approach to my development.

🤝 Collaboration

  • I am looking for collaboration opportunities in artificial intelligence and data science projects. Eager to be involved in innovative and challenging projects, I aim to apply my knowledge and skills in practical settings.

📬 You can reach me at this email: ahtabekdas@gmail.com

💡 My Data Science Experience

Mastering Applied Data Science with Deep Learning Bootcamp (08/2023-02/2024)
  • During this 6-month intensive bootcamp, I focused on advanced data science techniques, especially deep learning.
  • I developed expertise in designing, training, and evaluating deep neural networks for various applications, including image recognition, natural language processing, and time series analysis.
  • Thanks to hands-on experience with industry-standard tools and frameworks such as TensorFlow and Keras, I enhanced my ability to apply theoretical concepts to real-world projects.
  • I executed multiple real-world projects, demonstrating the ability to translate theoretical concepts into practical solutions.

🛠 Skills

  • Deep Learning: Neural Networks, CNNs, RNNs, GANs
  • Machine Learning: Supervised and Unsupervised Learning, Feature Engineering
  • Programming Languages: Python
  • Data Manipulation: NumPy, Pandas
  • Data Visualization: Matplotlib, Seaborn, Plotly
  • Tools/Frameworks: TensorFlow, Keras, scikit-learn, Pycaret
  • Metric Research: Experience in conducting metric research using GVSearch
  • Problem Solving: Analytical Thinking, Experimentation
  • Communication: Data Visualization, Technical Documentation

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  1. Obesity-Risk-Multi-Class-Prediction Obesity-Risk-Multi-Class-Prediction Public

    This project focuses on predicting the risk of obesity among individuals based on their eating habits and physical condition using various machine learning models. The objective is to classify indi…

    Jupyter Notebook

  2. German-Traffic-Sign-Recognition German-Traffic-Sign-Recognition Public

    In this project, we delve into the world of traffic sign recognition, with a specific focus on German road signs. Our goal is to develop a robust machine learning model capable of accurately identi…

    Jupyter Notebook

  3. Advanced-Regression-for-House-Price-Prediction Advanced-Regression-for-House-Price-Prediction Public

    In this project, we aim to develop a model capable of predicting house prices using state-of-the-art regression models and based on various features and attributes

    Jupyter Notebook

  4. Bart-Project-2016-2017 Bart-Project-2016-2017 Public

    This project focuses on addressing key data analytics questions related to the San Francisco BART (Bay Area Rapid Transit) project from 2016-2017.

    Jupyter Notebook

  5. CS-GO-Round-Winner-Prediction CS-GO-Round-Winner-Prediction Public

    In this project, we are focused on predicting the winners of rounds in the popular video game Counter-Strike: Global Offensive (CS:GO). By utilizing data and machine learning techniques, we aim to …

    Jupyter Notebook

  6. Water-Potability-Clustering-and-Classification Water-Potability-Clustering-and-Classification Public

    Assessing water safety with data from 3276 sources. Goals: Cluster similar water bodies, predict potability, inform health & policy experts.

    Jupyter Notebook 1