Advanced AI Training Codes

This repository contains hands-on AI & Machine Learning studies developed during the Ministry of Industry and Technology – Advanced AI Training Program.

The repository mainly consists of Jupyter Notebooks covering:

• Classical Machine Learning
• Computer Vision
• Time Series Feature Engineering
• Distributed ML with PySpark

---

📂 Repository Structure

🔹 Computer Vision

• Canny_Edge_Detection_and_Hough_Transform.ipynb
  Implementation of Canny Edge Detection and Hough Transform for line detection.

🔹 Machine Learning

• DigitRecognitionMachineLearning.ipynb
  Digit recognition pipeline using classical ML techniques.

• Digit_Recognition.ipynb
  Alternative digit recognition experiments and preprocessing variations.

🔹 Time Series & Finance

• FeatureEngineering&MachineLearningwithTimeSeriesFinanceDatas.ipynb
  Feature engineering and predictive modeling using financial time-series data.

🔹 Big Data & Distributed ML

• HouseValuePredictionwithSpark.ipynb
  House value prediction using PySpark MLlib, demonstrating distributed data processing and ML pipelines.

🔹 System / Architecture Concept

• ÇevrimiçiAlışverişSitesiAltyapısı.ipynb
  Conceptual work on online shopping system architecture and infrastructure ideas.

---

🛠 Tech Stack

• Python 3.9+
• NumPy
• Pandas
• Scikit-learn
• Matplotlib
• PySpark
• Jupyter Notebook / JupyterLab

---

⚙ Installation

1️⃣ Create a virtual environment (recommended)

python -m venv venv
source venv/bin/activate   # Mac/Linux
venv\Scripts\activate      # Windows