INDE 577 -- Data Science and Machine Learning

Hi! My name is Peter and this is my repository for the introduction to machine learning class. This repository will go through major algorithms in machine learning and put some major machine learning algorithms into practice.

I have played soccer ever since I was a kid, so to demonstrate these machine learning techniques, I will use soccer data from the 2022-2023 English Premeire League Season to combine this new, interesting topic, with something I love.

Course Overview:

This repository contains Jupyter Notebook projects demonstrating machine learning algorithms covered during the Spring 2024 semester at Rice University. The course topics include an introduction to scikit-learn, Keras, and TensorFlow, linear and logistic regression, clustering, support vector machines, random forest trees, neural networks, deep learning, and unsupervised learning.

Types of Machine Learning Algorithms:

The machine learning algorithms discussed in this course fall into two main categories:

1. Supervised Learning:

Supervised learning algorithms learn from labeled data and are used for tasks such as classification and regression. Examples of supervised learning algorithms covered in this repository include:

• Perceptron
• Linear Regression
• Gradient Descent
• Logistic Regression
• Neural Networks
• K Nearest Neighbors
• Decision/Regression Trees
• Ensemble Learning

2. Unsupervised Learning:

Unsupervised learning algorithms learn patterns from unlabeled data and are used for tasks such as clustering and dimensionality reduction. Examples include:

• K-Means Clustering
• Principle Component Analysis

Further Reading:

For further reading and resources, please refer to the following:

• Hands-On Machine Learning with Scikit-learn, Keras, and TensorFlow
• Machine Learning with Python for Everyone, Addison Wesley Data & Analytics Series, 2020 Pearson Education, by Mark E. Fenner

Resources:

• Python Jupyter Notebooks.
• Common data science libraries such as Pandas, Matplotlib, Seaborn, NumPy, and Scikit-Learn.
• ChatGPT and Github Copilot (In accordance with the course rules).
• Dr. Davila's course materials, which can be found on youtube here.

Conclusions:

For me, this repository served as a way for me to understand machine learning algorithms. I hope for others, it will serve as a practical resource for understanding and implementing machine learning algorithms within the context of data science, providing valuable insights into the applications and techniques of this rapidly evolving field.

I'm excited to continue my journey with machine learning, but, if nothing else, I hope I get just a little bit better at fantasy fútbol :)