Machine Learning AI Project

Description

This project is dedicated to learning and developing skills in the field of Machine Learning. Here, we explore various models and algorithms in Machine Learning.

Structure

• Data Science :
  • Pandas Exploring the functionalities of the Pandas library for data manipulation Data Frame.
  • Finance in Data Frame Exploring the functionalities of the Pandas library for data manipulation in Finance.
  • Matplotlib Exploring the functionalities of the Matplotlib library for data visualization.
  • NumPy Exploring the functionalities of the NumPy library for numerical operations.
  • SeaBorn Exploring the functionalities of the SeaBorn library for data visualization.
  • Statistics Exploring the functionalities of the Statistics library for statistical operations.

Machine Learning :

Data Preprocessing

Exploring the functionalities of the Scikit-learn library for data preprocessing.

Regression

Exploring the functionalities of the Scikit-learn library for regression models.

• Linear Regression
• Polynomial Regression
• Support Vector Regression
• Decision Tree Regression
• Random Forest Regression

Classification

Exploring the functionalities of the Scikit-learn library for classification models.

• Logistic Regression
• K-Nearest Neighbors
• Support Vector Machine
• Kernel SVM
• Naive Bayes
• Decision Tree Classification
• Random Forest Classification

Clustering

Exploring the functionalities of the Scikit-learn library for clustering models.

• K-Means
• Hierarchical Clustering

Association Rule Learning

Exploring the functionalities of the Scikit-learn library for association rule learning.

• Apriori
• Eclat

Reinforcement Learning

Exploring the functionalities of the Scikit-learn library for reinforcement learning.

• Upper Confidence Bound
• Thompson Sampling

Natural Language Processing

Exploring the functionalities of the Scikit-learn library for natural language processing.

• Bag of Words

Deep Learning

Exploring the functionalities of the Scikit-learn library for deep learning.

• Artificial Neural Networks
• Convolutional Neural Networks

Dimensionality Reduction

Exploring the functionalities of the Scikit-learn library for dimensionality reduction.

• Principal Component Analysis
• Linear Discriminant Analysis
• Kernel PCA

Model Selection

Exploring the functionalities of the Scikit-learn library for model selection.

• CatBoost
• XGBoost
• LightGBM

How to Use This Project

1. Clone this repository to your local computer after obtaining approval from the author!
2. Install the necessary dependencies using pip install -r requirements.txt.
3. Open and run each notebook in a Python development environment (preferably Anaconda or Jupyter Notebook).
4. Explore the code.

License

This project is licensed under the Personal License

Issues

If you encounter any issues or have questions regarding this project, please feel free to contact the author with the problem. I will try to respond and provide assistance as soon as possible.

Additional Resources

For further knowledge in Machine Learning, you can refer to the following resources:

• Pandas Official Documentation
• TensorFlow Official Documentation
• Scikit-learn Official Documentation
• Matplotlib Official Documentation
• NumPy Official Documentation
• SeaBorn Official Documentation

Author

This project was created and is maintained by Bahna Darius. You can find me on LinkedIn.