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🚀 Machine Learning Notebooks Repository 📚

Machine Learning Notebooks

👋 Hello! Welcome to my Machine Learning Notebooks repository! 📚 This repository contains a collection of machine learning notebooks that I implemented during my master's program machine learning course. Each notebook represents a laboratory exercise that covers different machine-learning techniques and algorithms. The notebooks are intended to serve as a personal reference and showcase my mastery of the course material. Whether you are a beginner or an experienced data scientist, these notebooks can be valuable resources to review and reinforce your understanding of machine learning concepts.

Notebooks Overview

📝 Below is an overview of the laboratory exercises included in this repository:

1. Decision Tree 🌳:

  • This notebook implements a Decision Tree classifier from scratch using a popular dataset. It covers topics such as information gain, entropy, and tree pruning techniques to prevent overfitting.

2. Linear Regression 📈:

  • The Linear Regression notebook demonstrates how to perform simple linear regression and multiple linear regression using the least squares method. It also includes model evaluation techniques and interpretation of results.

3. Principal Component Analysis (PCA) 📊:

  • In this notebook, we explore Principal Component Analysis (PCA), a dimensionality reduction technique. It covers the mathematical concepts behind PCA and its application to reduce the feature space while preserving essential information.

4. Supervised Classification using Hand-Crafted Features ✋:

  • This notebook focuses on supervised classification using hand-crafted features. It demonstrates how to extract relevant features from the data and build a classifier to predict target labels.

5. Image Classification using Convolutional Neural Networks (CNN) 🖼️:

  • The CNN notebook showcases image classification using Convolutional Neural Networks, a deep learning approach. It employs a popular dataset and TensorFlow/Keras to build and train the CNN model.

6. Random Forest and XGBoost 🌳:

  • This notebook explores ensemble methods such as Random Forest and XGBoost. It compares their performances on a dataset and explains the advantages of using ensemble techniques.

7. Support Vector Machines (SVM) 🎯:

  • In this notebook, we delve into Support Vector Machines (SVM), a powerful classification algorithm. It covers the theory behind SVM and its implementation using scikit-learn.

8. Anomaly Detection ⚠️:

  • The Anomaly Detection notebook introduces various techniques to detect anomalies in data. It includes methods such as isolation forests and local outlier factor.

9. Fully Connected Neural Network 🧠:

  • This notebook implements a Fully Connected Neural Network using Keras. It demonstrates how to build, train, and evaluate a neural network on a real-world dataset.

How to Use the Notebooks

🚀 These notebooks are self-contained and require no additional installation. You can easily explore and interact with them using Jupyter Notebook or JupyterLab. Follow the steps below to get started:

  1. Clone the repository to your local machine:
git clone https://github.com/your-username/machine-learning-notebooks.git

cd machine-learning-notebooks

  1. Open Jupyter Notebook or JupyterLab:

    a. If you have Jupyter Notebook installed, run:

    jupyter notebook

    b. If you have JupyterLab installed, run:

    jupyter lab

  2. Browse to the notebook you want to review and click on it to open it.

  3. Read through the notebook and review the code, explanations, and visualizations.

License and Acknowledgments

⚠️ Please note that this repository is not open for contribution, as it contains your laboratory exercises from the master's program. The notebooks are personal works and should not be shared or reproduced without proper authorization.

The repository and its contents are protected under all applicable copyright laws. All rights are reserved to you, the author and contributor of the notebooks.

Contact Information

📧 If you have any questions or need further assistance regarding the notebooks or the repository, feel free to contact me at adeolajosepholoruntoba@gmail.com.

Thank you for exploring this repository, and I hope these notebooks help you in your machine learning journey and future endeavors. Happy learning and continued success in your master's program!