emirkaanozdemr Goto Github PK

followers: 1.6K following: 5.7K repos: 108.0 gists: 0.0

Name: Emir Kaan Özdemir

Type: User

Bio: Data Scientist | AI | Astronomy and Quantum Computing Enthusiast | High School Student at Istanbul Boys' High School | I'm an intern at @caltech

Location: Istanbul, Turkey

Blog: https://bit.ly/ekozdemir

Welcome to My GitHub Profile!

About Me

I am a student at Istanbul Erkek Lisesi with a strong passion for quantum physics, computing, and astronomy. Alongside my academic pursuits, I hold a professional certification in data science which has equipped me with advanced skills in various areas of data science and machine learning.

Interests

Quantum Physics and Computing: I am deeply fascinated by the principles of quantum mechanics and their applications in computing. My interest lies in exploring how quantum algorithms can transform traditional computational methods.
Astronomy: I have a keen interest in the cosmos and celestial phenomena. I enjoy stargazing and learning about the universe's mysteries, from planetary science to deep space exploration.
Data Science: My expertise includes predictive analytics, machine learning models, and reinforcement learning. I have a solid foundation in classical and advanced data science methodologies.

Skills and Expertise

Programming Languages and Frameworks: Python, JavaScript, HTML, CSS, Bootstrap, Django, Streamlit, Flask, jQuery, Visual Basic .NET, C and Q#.
Tools and Technologies: Experienced with Tensorflow, Pytorch, Scikit-Learn, Spark, Numpy, Pandas, Matplotlib, Seaborn, Plotly Express, OpenCV, Conda, Jupyter, Astropy and various database management systems.

Contact Me

Feel free to reach out via email to me

Languages, Markup Lanimguages and Frameworks

Python	JavaScript	HTML5	CSS3	Bootstrap	jQuery	Visual Basic .NET	C	Q#	Docker

Python3 Libraries and Frameworks

Scikit-Learn	Spark	Numpy	Pandas	Matplotlib	Seaborn	Plotly Express	OpenCV	Astropy	Streamlit	Flask	Django

Deep Learning

Tensorflow	PyTorch

Quantum Computing

Q#	IBM Qiskit

Platforms

Conda	Jupyter	VS Code

Emir Kaan Özdemir's Projects

a-basic-chatbot-using-rnn-lstm-

This project involves developing a basic chatbot using RNN (LSTM). The chatbot is trained on various datasets to handle different types of user interactions. It leverages LSTM's capability to process sequential data, making it effective in understanding and generating natural language responses.

a-job-recommender-system

a-recommender-system-popularity-based

ads-click-through-rate-prediction

amazon-reviews-analysis

anti-controlled-gates

New think I learned from microsoft quantum katas: anti-controlled gates.

arbitary-controls

New think I learned from microsoft quantum katas: arbitary controlls.

article-recommendation-system

article-scoring

auto-computer-vision

barcode-and-qr-code-reader

bell-states

Bell State Changes and creating Bell States from |00> with Q#. It is impossible, creating Bell States using single-qubit systems because all of the Bell States are entangled.

binary-classification-with-a-software-defects-dataset

brew

🍺 The missing package manager for macOS (or Linux)

building-a-predictive-maintenance-model-with-smote-

caesar-shift-encryption

In this project, I tried to implement the Caesar cipher encryption method in Python, allowing text to be encrypted with a specified shift value.

classification-with-an-academic-success-dataset

controlled-rotation

covid19-global-forecasting-with-automl

creating-a-dog-photo-as-a-painting-using-neural-style-transfer

In this project I tried to create a dog picture as a Painting using Neural Style Transfer.

creating-human-faces-with-a-vanilla-gan

creating-unequal-superposition

credit-card-accounts-segmentation

credit-card-fraud-detection-with-automl

credit-scoring-and-segmentation-using-python

dog-breed-identification

emirkaanozdemr

Who am I?

entangle-qubits-using-cnot

even-or-odd-basis-vectors

face-mask-detection-using-resnet50

This project uses transfer learning with the ResNet50 model to develop an efficient face mask detection system. By leveraging pre-trained weights and data augmentation, the model accurately classifies images as masked or unmasked, contributing to public health and safety by ensuring mask-wearing compliance.