Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/mdshimulmahmud/machine-learning-projects
https://github.com/mdshimulmahmud/machine-learning-projects
deep-learning machine-learning machine-learning-algorithms mlproject
Last synced: 5 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/mdshimulmahmud/machine-learning-projects
- Owner: MdShimulMahmud
- Created: 2023-05-15T06:06:07.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-05-02T17:08:50.000Z (7 months ago)
- Last Synced: 2024-05-03T03:56:46.139Z (7 months ago)
- Topics: deep-learning, machine-learning, machine-learning-algorithms, mlproject
- Language: Jupyter Notebook
- Homepage:
- Size: 2.74 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Machine Learning and Deep Learning Projects Repository
Welcome to the Machine Learning and Deep Learning Projects Repository! This repository serves as a collection of practical projects showcasing various machine learning and deep learning techniques. Each project provides hands-on experience and helps you improve your skills in the field.
## Table of Contents
- [Project 1: Sonar Rock vs Mine Prediction using Logistic Regression](./project1)
- [Project 2: Diabetes Prediction using SVM](./project2)
- [Project 3: Multivariate Linear Regression](./project3)
- [Project 4: Decision Tree](./project4)
- [Project 5: K-means Clustering](./project5)
- [Project 6: Apriori Algorithm](./project6)
- [Project 7: Backpropagation from scratch using synthetic data](./project7)
## Project Structure
Each project follows a similar structure:
- **Description**: A detailed overview of the project, including its objectives, techniques utilized, and notable findings.
- **Notebooks**: Jupyter notebooks containing the project code, including data preprocessing, model training, evaluation, and visualization.
- **Data**: Data files or instructions on acquiring the required datasets for the project.
- **Results**: Results, such as trained models, evaluation metrics, and visualizations.
## Getting Started
To begin a project, navigate to its directory and refer to the instructions provided in the project's README.md file. Ensure that you have the necessary dependencies installed and any required datasets downloaded.