Machine Learning Playground.
Welcome to the Machine Learning Playground! This repository is a collection of small machine learning projects that serve as a playground for exploring various concepts and techniques in the field of machine learning. Whether you are a beginner looking to dive into machine learning or an experienced practitioner wanting to experiment with new ideas, this repository is a great place to start.
Table of Contents Project 1: Linear Regression Project 2: Image Classification with CNN Project 3: Sentiment Analysis with LSTM Adding Your Own Projects Contributing.
Project 1: Handwritten Digits Recognition using MNIST Dataset Description: A convolutional nueral network(CNN) implementation using a synthetic dataset. This project help the user be thorough with the basics of keras and neural networks and how to build a neural network & interpret the results.
Project 2: Image Classification with CNN Description: Implement a convolutional neural network (CNN) for image classification using a popular dataset like MNIST or CIFAR-10. Experiment with different architectures and hyperparameters. It contains two models one made on Google Collab, this model classifies dogs and cats with the help of suoervised machine learning dataset is taken from kaggle. The other model is also a supervised machine learning model which is made on jupyer notebook, this model classisifies happy and sad people.
Adding Your Own Projects Feel free to contribute to this repository by adding your own machine learning projects! To do so, follow these steps:
Fork the repository. Create a new directory for your project within the projects folder. Include a README.md file in your project directory explaining the purpose, dataset (if applicable), and any specific instructions. Add your code and any necessary files to the project directory. Update this README.md file with a brief description of your project under the "Adding Your Own Projects" section. Contributing Contributions are welcome! Whether you want to add new projects, improve existing ones, or fix bugs, please follow the steps in the Contributing Guidelines