Data-Science-Mini-Projects

Overview

Welcome to the "Data-Science-Mini-Projects" repository! This collection serves as a curated list of links to individual GitHub repositories, each containing a unique mini-project related to Data Science. Whether you are a beginner looking to gain hands-on experience or an experienced practitioner seeking inspiration, these mini-projects cover a range of topics within the field of Data Science.

Table of Contents

• Introduction
• How to Use
• List of Mini-Projects
• Contributing
• License
• Contact
• About Me

Introduction

Explore a variety of mini-projects to strengthen your Data Science skills. Each project is hosted in its own GitHub repository, providing detailed documentation, code, and datasets for you to dive into.

How to Use

1. Browse the list of mini-projects below.
2. Click on the project title to access the corresponding GitHub repository.
3. Explore the project's README for instructions, code, and any additional resources.

List of Mini-Projects

NLP Projects:

1. LLM Projects Hub

Deep Learning Projects:

1. Images' Web-Scraping using Selenium-Python: Utilizes Selenium in Python for web scraping of images from the internet.

2. Face-Mask Detection using CNN: Face Mask Detection using Convolutional Neural Networks for identifying individuals wearing masks in images.

3. Lungs Segmentation using ResNet50 and DeeplabV3: Lung Segmentation using ResNet50 and DeeplabV3 for precise extraction of lung regions in medical chest X-ray images.

4. Build a CNN for Image Classification: Building a Convolutional Neural Network (CNN) for image classification tasks.

5. Build CNN using Transfer Learning: Building a Convolutional Neural Network (CNN) using Transfer Learning for efficient image classification.

6. Image Segmentation using U-Net Model: Image Segmentation using the U-Net Model for precise delineation of objects and structures in images.

7. Build YOLO Model from scratch: Building a YOLO (You Only Look Once) model from scratch for efficient real-time object detection.

8. Modern SOTA CNN Architectures: Implementing modern state-of-the-art Convolutional Neural Network (CNN) architectures with a focus on interpretability.

9. Fashion-MNIST Classification using Dense-Neural-Network: Classifies Fashion MNIST dataset using Dense Neural Networks in Keras.

10. Image Classification with Vision-Transformer: This repository demonstrates how to perform image classification with Vision-Transformer.

11. Introduction to Stable-Diffusion: This repository introduces to Stable-diffusion and talks about basics it.

12. Introduction to Conditional-GANs: This repository introduces to Conditional-GANs(cGANs) and talks about basics it.

Machine Learning Projects:

1. Loan Approval Classification using Advanced Techniques: Implement Loan Approval Classification using Advanced Techniques.
2. Employee Attrition Mini-Project: Analyzes and addresses employee attrition in a mini project.
3. Acoustic Extinguisher Fire: Explores the use of acoustic signals for fire detection and extinguishing.
4. Employee Abseentism Mini-Project: Investigates and manages employee absenteeism in a comprehensive project.
5. House-Prices Prediction: Advanced Regression Techniques: Applies advanced regression techniques to predict house prices with 80 input features.
6. Bike Sharing demand Mini-Project: Optimizes bike-sharing systems through advanced algorithms, leveraging data-driven approaches for distribution and user experience improvement.

Computer Science Projects:

1. Introduction to OpenMP: This repository provides an introduction to using OpenMP for parallelization in Python. The notebook demonstrates how to implement multiprocessing using OpenMP, along with the necessary concepts and examples.

2. OOPs in Python : This repository demonstrates the general structure of classes in Object-Oriented Programming (OOP) using Python. It aims to help you learn how to build your own classes, specialized to your needs.

3. Parallel Programming with MPI : This repository demonstrates the implementation of standard message-passing algorithms using MPI (Message Passing Interface). It aims to help you understand the basics of point-to-point communication, blocking and non-blocking communication, and collective communication, along with their impact on program performance.

4. Time Complexity Analysis: This repository contains a Jupyter Notebook that demonstrates the concept of computational complexity, focusing on time complexity and Big-O notation. It aims to help you understand these fundamental concepts and determine the time complexity of given algorithms.

5. Monitoring Resources Using Psutil: This repository demonstrates how to monitor various resources of your device using the psutil package in Python. This includes monitoring CPU, GPU, memory, disks, network, and sensors. Additionally, it explores the multiprocessing package to evaluate the advantages of parallelism in resource monitoring.

Feel free to contribute by adding your own mini-projects to the list!

Contributing

If you have a Data Science mini-project that you'd like to share, please follow the guidelines in CONTRIBUTING.md.

Code of Conduct

Please adhere to our Code of Conduct in all your interactions with the project.

License

This project is licensed under the MIT License.

Contact

For questions or inquiries, feel free to contact me on Linkedin.

About Me:

I’m a seasoned Data Scientist and founder of TowardsMachineLearning.Org. I've worked on various Machine Learning, NLP, and cutting-edge deep learning frameworks to solve numerous business problems.