https://github.com/jibanul/ml-projects

AI/ML projects covering traditional data science, and deep learning for structured and unstructured data.
https://github.com/jibanul/ml-projects

ai data-science ml

Last synced: about 1 month ago
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AI/ML projects covering traditional data science, and deep learning for structured and unstructured data.

• Host: GitHub
• URL: https://github.com/jibanul/ml-projects
• Owner: Jibanul
• License: mit
• Created: 2021-02-01T04:12:04.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2025-03-17T02:56:58.000Z (over 1 year ago)
• Last Synced: 2025-03-17T03:32:43.441Z (over 1 year ago)
• Topics: ai, data-science, ml
• Language: R
• Homepage:
• Size: 7.16 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 🧠 Machine Learning Projects Repository

Welcome to my **Machine Learning Projects** repository! This repo contains various ML projects that I have worked on during my graduate studies and beyond. The projects cover a wide range of topics, from traditional data science models to AI applications involving unstructured data such as images, videos, and text.

## 📂 **Project Organization**

Each project is structured as a separate folder, following a numerical system for easy navigation:

- **1.x - Traditional Machine Learning (Data Science)**

  - Includes projects related to structured data, tabular datasets, and classical ML techniques.

  - Examples: Regression, Classification, Time Series Forecasting, Feature Engineering.

- **2.x - AI for Unstructured Data (Images, Videos, Text)**

  - Focuses on Deep Learning, Computer Vision, Natural Language Processing, and advanced AI models.

  - Examples: Object Detection/Classification/Counting, Text Summarization, Video Analysis, and 3D Point Cloud Processing.

## 🛠 **Technologies Used**

- **Traditional ML:** Scikit-learn, Pandas, NumPy, XGBoost, etc.

- **Deep Learning:** PyTorch, TensorFlow, OpenCV, Hugging Face, etc.

- **Other Tools:** SQL, Flask, FastAPI, Docker, etc.

## 🚀 **How to Use**

1. Clone the repository:

   ```bash

   git clone https://github.com/your-username/ml-projects.git