Machine Learning from Data 🤖📊

Welcome to the repository for the Machine Learning course! This repository contains resources, code examples, datasets, and assignments to help you learn and understand various topics in machine learning. In this readme file, we will provide an overview of the repository and instructions on how to navigate and utilize its contents effectively.

Repository Structure

The repository is organized into the following main topics:

1. Linear Regression: This folder contains code examples, datasets, and assignments related to linear regression. Linear regression is a fundamental technique in machine learning used for predicting a continuous target variable based on one or more input features.

2. Decision Tree: This folder focuses on decision trees, which are powerful models for both classification and regression tasks. You will find code examples, datasets, and assignments to understand and implement decision trees.

3. Density Estimation: This folder explores density estimation techniques, such as kernel density estimation and Gaussian mixture models. You will find code examples, datasets, and assignments to learn how to estimate probability densities from data.

4. Logistic Regression & Naive Bayes: This folder covers two important classification algorithms, logistic regression, and Naive Bayes. You will find code examples, datasets, and assignments to understand and implement these algorithms.

5. Clustering: This folder focuses on clustering techniques, such as k-means clustering and hierarchical clustering. You will find code examples, datasets, and assignments to learn how to group similar data points together based on their characteristics.

Each topic folder contains subfolders for code examples, datasets, and assignments specific to that topic. You can explore these folders to access the relevant materials for your learning journey.

Getting Started

To get started with this repository, follow these steps:

1. Clone the repository to your local machine using the command:

   git clone https://github.com/EthanFajnkuchen/Machine-Learning-From-Data.git
   

   Alternatively, you can download the repository as a ZIP file and extract it to a directory of your choice.

2. Navigate to the desired topic folder to access the code examples, datasets, and assignments related to that topic.

3. Review the code examples to understand the implementation of various algorithms and techniques in machine learning.

4. Explore the datasets provided in each topic folder to practice applying the learned concepts and algorithms on real-world data.

5. Complete the assignments to test your understanding and reinforce your knowledge. Instructions for each assignment can be found in the respective folders.

We hope this repository helps you in your journey to learn and master machine learning techniques. Happy learning!