Welcome to the repository containing the teaching content I created for a class I taught earlier this year. This repository includes eight Jupyter notebooks that cover various topics in machine learning and data science.
This notebook introduces the basics of databases and linear regression, providing a foundation for data storage and simple predictive modeling.
This notebook covers various fundamental concepts essential for understanding more advanced topics in machine learning and data science.
This notebook focuses on NumPy, a fundamental tool for machine learning. It covers data handling using NumPy, which is essential for ML projects.
This notebook introduces the perceptron and artificial neural networks (ANNs), explaining their significance and how they work.
This notebook delves into unsupervised learning, discussing various techniques and their applications.
This notebook specifically focuses on Convolutional Neural Networks (CNNs), explaining their structure and how they are used in image processing tasks.
This notebook covers decision trees and tree ensembles, explaining their use in classification and regression tasks.
This notebook discusses embedding models and Long Short-Term Memory (LSTM) networks using Keras, providing insights into handling sequential data.
- Clone the repository:
git clone https://github.com/pkgorde/Machine-Learning-for-Non-Majors.git
Navigate to the repository directory:
cd Machine-Learning-for-Non-Majors
Open the desired notebook in Jupyter:
jupyter notebook Discussion_X.ipynb
Replace X with the corresponding discussion number.
License This repository is licensed under the MIT License. See the LICENSE file for more details.
Contributions Contributions are welcome! Please feel free to submit a Pull Request or open an Issue for any suggestions or improvements.