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Machine-Learning-for-Non-Majors

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.

Notebooks Overview

1. Discussion 1: Database basics and Linear Regression

This notebook introduces the basics of databases and linear regression, providing a foundation for data storage and simple predictive modeling.

2. Discussion 2

This notebook covers various fundamental concepts essential for understanding more advanced topics in machine learning and data science.

3. Discussion 3: NumPy

This notebook focuses on NumPy, a fundamental tool for machine learning. It covers data handling using NumPy, which is essential for ML projects.

4. Discussion 4: The Perceptron and ANNs

This notebook introduces the perceptron and artificial neural networks (ANNs), explaining their significance and how they work.

5. Discussion 5: Unsupervised Learning

This notebook delves into unsupervised learning, discussing various techniques and their applications.

6. Discussion 6: Convolutional Neural Networks

This notebook specifically focuses on Convolutional Neural Networks (CNNs), explaining their structure and how they are used in image processing tasks.

7. Discussion 7: Decision Tree and Tree Ensembles

This notebook covers decision trees and tree ensembles, explaining their use in classification and regression tasks.

8. Discussion 8: Embedding models and LSTMs with Keras

This notebook discusses embedding models and Long Short-Term Memory (LSTM) networks using Keras, providing insights into handling sequential data.

How to Use

  1. 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.

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