🚀 AI Examples Repository

Welcome to the AI Examples Repository! This repository contains various machine learning and deep learning examples using Scikit-Learn and TensorFlow.

📌 About

This repository includes multiple AI-related examples, covering topics like: ✅ Supervised Learning (Classification, Regression) ✅ Neural Networks (Deep Learning) ✅ Convolutional Neural Networks (CNNs) ✅ Model Training & Prediction

All examples are implemented in Python 3.11 and demonstrate how to build and train machine learning models efficiently.

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📂 Examples Included

1️⃣ Logistic Regression with Scikit-Learn

• Implements classification using Logistic Regression.
• Uses Iris Dataset from sklearn.datasets.
• Demonstrates data preprocessing, model training, evaluation, and predictions.

2️⃣ CNN for Image Classification with TensorFlow

• Builds a Convolutional Neural Network (CNN) using tensorflow.keras.
• Trains on the CIFAR-10 dataset to classify images into 10 categories.
• Includes data normalization, model architecture, training, and custom image prediction.
• Supports saving and loading models to avoid retraining every time.

3️⃣ Transformer Model for Sentiment Analysis with TensorFlow

• Implements a Transformer-based neural network using tensorflow.keras.
• Trains on the IMDB Movie Reviews Dataset for binary sentiment classification (positive or negative).
• Includes:
  • Data downloading and preprocessing.
  • Custom Transformer architecture with positional embeddings and multi-head attention.
  • Functions for model training, prediction, and evaluation.

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⚙️ Installation & Setup

Before running the code, install the required dependencies:

pip install numpy pandas matplotlib scikit-learn tensorflow opencv-python pillow

Ensure that you're using Python 3.11:

python --version

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🚀 Running the Examples

Run each of the examples individually and see the result.

python linear_regression.py

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📌 Saving & Loading Models

To avoid re-training the model every time, the "cnn_image_classification_custom_image.py" script automatically saves the trained model as:

cifar10_model.h5

If the model is already trained, it will be loaded automatically instead of re-training.

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🎯 Future Improvements

• Adding Object Detection models.
• Implementing Reinforcement Learning algorithms.

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📝 License

This project is open-source under the MIT License. Feel free to use, modify, and contribute! 🚀

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🔗 Stay Connected For updates and discussions, feel free to follow the repository or open an issue if you have any questions!