AI and ML Code Repository

This repository contains a collection of basic Artificial Intelligence (AI) and Machine Learning (ML) codes and implementations. The codes range from simple algorithms to more advanced models, demonstrating the key concepts in AI and ML, such as regression, classification, clustering, deep learning, and reinforcement learning.

Contents

• Supervised Learning
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Support Vector Machines (SVM)
• Unsupervised Learning
  • K-Means Clustering
  • Hierarchical Clustering
• Deep Learning
  • Neural Networks (Feedforward, Convolutional)
  • TensorFlow / Keras Example
• Reinforcement Learning
  • Q-learning
• Data Preprocessing
  • Feature Scaling
  • Encoding Categorical Data
  • Handling Missing Data

Requirements

Before you start, ensure you have the following libraries installed:

• Python (>= 3.x)
• numpy
• pandas
• scikit-learn
• matplotlib
• seaborn
• tensorflow (for deep learning models)
• keras (for deep learning models)
• jupyter (optional, for running Jupyter notebooks)

You can install the dependencies using pip:

pip install -r requirements.txt

text
Copy code
numpy
pandas
scikit-learn
matplotlib
seaborn
tensorflow
keras
jupyter
Usage

To use the codes in this repository, clone the repo to your local machine:

git clone https://github.com/yourusername/ai-ml-codes.git

Then navigate to the specific directory or notebook you want to explore and follow the instructions in the respective file.

Example - Linear Regression

python

# Import necessary libraries

import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

# Load dataset
data = pd.read_csv('data.csv')

# Prepare the data
X = data['feature'].values.reshape(-1, 1)
y = data['target'].values

# Create and train the model
model = LinearRegression()
model.fit(X, y)

# Make predictions
predictions = model.predict(X)

# Plotting the results
plt.scatter(X, y, color='blue')
plt.plot(X, predictions, color='red')
plt.show()
Example - Neural Network using Keras
python
Copy code
from keras.models import Sequential
from keras.layers import Dense
import numpy as np

# Load dataset (example)
X_train = np.random.rand(100, 5)
y_train = np.random.randint(2, size=100)

# Define the model
model = Sequential()
model.add(Dense(64, input_dim=5, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# Compile the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])



# Train the model

model.fit(X_train, y_train, epochs=10, batch_size=10)
Directory Structure

  ai-ml-codes/
├── supervised_learning/
│   ├── linear_regression.py
│   ├── logistic_regression.py
│   └── decision_tree.py
├── unsupervised_learning/
│   ├── kmeans_clustering.py
│   └── hierarchical_clustering.py
├── deep_learning/
│   ├── neural_network.py
│   └── cnn_model.py
├── reinforcement_learning/
│   ├── q_learning.py
├── data_preprocessing/
│   ├── feature_scaling.py
│   └── missing_data_handling.py
└── requirements.txt

Contributing

Contributions are welcome! If you have any suggestions or improvements, feel free to fork this repository and submit a pull request.

Here are some ways you can contribute:

Add new algorithms or models

Improve the documentation

Fix bugs or improve the existing code

Please make sure to follow the code style and include tests if necessary.

License

This project is licensed under the MIT License - see the Licence for details.

Acknowledgements

This repository is intended to help beginners understand basic AI and ML concepts. Many of the algorithms are based on widely available resources, including books and tutorials.

Contact

For any inquiries or suggestions, please feel free to open an issue or contact me via GitHub.

This template provides a solid foundation to document your AI and ML code. Feel free to adapt it based o

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