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Achievement: Starstruckx2

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Achievement: Starstruckx2

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@OnlineBankingSytem-Team @Global-AI-Hub-Bootcamp

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MelihGulum/README.md

Hello 🤙 I'm Melih GULUM

A passionate machine learning and deep learning developer from Turkey

  • 🔭 I’m currently studying Computer Science

  • 🌱 I’m currently learning machine learning algorithms in tensorflow and neural language processing

  • 👨‍💻 All of my projects are available at my website

  • 📝 I regularly write articles on my personal Medium account

  • 💬 Ask me about anything!

☕ Connect with me:

melih-gulum melihgulum melihgulum @melih-gulum

🧰 Languages, Tools and Frameworks:

java python

mssql postgresql mysql

html html javascript flask

tableau powerbi pandas seaborn matplotlib beautifulsoup selenium

librosa tkinter numpy scipy opencv pytorch scikit_learn tensorflow optuna xgboost catboost lightgbm

📈 Github Stats:

GitHub Stats Most Used Languages

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  1. Comprehensive-Data-Science-AI-Project-Portfolio Comprehensive-Data-Science-AI-Project-Portfolio Public

    A curated collection of AI, data engineering, and DevOps projects featuring real-world applications, advanced techniques, and tutorials—ideal for learners and practitioners exploring data science a…

    Jupyter Notebook 149 25

  2. Music-Genre-Classification Music-Genre-Classification Public

    This project aims to classify music genres. CNN architecture and GTZAN dataset were used for model training. Finally, a Web Application was made with Flask.

    Jupyter Notebook 15 16

  3. ASL-Recognition-CNN-OpenCV ASL-Recognition-CNN-OpenCV Public

    American Sign Language Alphabet recognition with Deep Learning's CNN architecture

    Jupyter Notebook 5 5

  4. Understanding-Librosa Understanding-Librosa Public

    Jupyter Notebook 5

  5. CIFAR-10-CNN-FLASK-Deployment CIFAR-10-CNN-FLASK-Deployment Public

    Classification of CIFAR dataset with CNN which has %91 accuracy and deployment of the model with FLASK.

    Jupyter Notebook 5 1

  6. Sentiment-Analysis-and-Spam-Classification Sentiment-Analysis-and-Spam-Classification Public

    In this project, sentiment analysis is made from the sentences of the model trained with LSTM and also SMS classification is made with Naive Bayes. Thereafter, a Web Application was made with Flask.

    Jupyter Notebook 4 1