AI-From-University 🎓💻

This repository contains academic projects, assignments, and exercises from my university coursework in Artificial Intelligence. It showcases various AI concepts, algorithms, and models implemented during the learning process, providing a practical approach to AI theories.

Table of Contents

• Overview
• Features
• Installation
• Technologies Used
• Challenges Faced
• Lessons Learned
• Contributing
• License
• Contact

Overview 📖

The AI-From-University repository contains a collection of Artificial Intelligence (AI) projects developed as part of university coursework. The projects involve various AI algorithms, including supervised and unsupervised learning, neural networks, and more. This repository aims to help students and enthusiasts gain insights into AI models and their practical applications.

Features ✨

• Implementation of key AI algorithms.
• Hands-on projects for supervised and unsupervised learning.
• Neural network examples and deep learning models.
• Well-documented code and descriptions for each project.

Installation ⚙️

To get started with this repository, follow these steps:

1. Clone the repository:

   git clone https://github.com/Md-Emon-Hasan/AI-From-University.git

2. Navigate to the project directory:

   cd AI-From-University

3. Install the required dependencies:

   pip install -r requirements.txt

Technologies Used 🛠️

• Python: Programming language for implementing AI models.
• NumPy and Pandas: Libraries for data manipulation and analysis.
• TensorFlow and Keras: For neural network and deep learning models.
• scikit-learn: For classical machine learning algorithms.

Challenges Faced 🤔

• Understanding the theoretical concepts behind AI algorithms and their practical applications.
• Optimizing neural networks and improving model accuracy.
• Working with large datasets and ensuring efficient computation.

Lessons Learned 📚

• Gained practical knowledge of various AI algorithms and models.
• Improved skills in Python programming and libraries like TensorFlow and scikit-learn.
• Developed a deeper understanding of machine learning concepts, such as overfitting, regularization, and hyperparameter tuning.

Contributing 🤝

Contributions are welcome! If you'd like to contribute to this repository, feel free to open an issue or submit a pull request.

License 📜

This repository is licensed under the GNU General Public License v3.0. See the LICENSE file for more details.

Contact 📧

• Email: iconicemon01@gmail.com
• WhatsApp: +8801834363533
• GitHub: Md-Emon-Hasan
• LinkedIn: Md Emon Hasan
• Facebook: Md Emon Hasan

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