Creating a preview or README for machine learning project on GitHub is a crucial step to communicate project's purpose, functionality, and usage to potential collaborators and users. Below is a preview of what my GitHub project README might include:
This repository contains a collection of machine learning algorithms implemented from scratch in Python. These implementations serve as a learning resource and a reference for understanding the fundamental concepts behind various machine learning techniques. Whether you're a beginner looking to learn the basics or an experienced practitioner interested in a deeper understanding, this repository can be a valuable resource.
Machine learning algorithms are at the core of many data-driven applications, from predictive modeling to pattern recognition. Implementing these algorithms from scratch provides insights into how they work and allows for customization and experimentation beyond what's possible with pre-built libraries. This repository aims to demystify machine learning by offering clear, concise, and well-documented implementations of various algorithms.
The following machine learning algorithms are included in this repository:
- Linear Regression (also with L1 and L2 Regularization)
- Logistic Regression
- k-Nearest Neighbors (k-NN)
- C-Nearest Neighbors (C-NN)
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- Principal Component Analysis (PCA)
- Neural Networks (Multi-Layer Perceptron)
- Hierarchical Agglomerative Clustering
- KMeans Clustering
Each algorithm is organized into its own directory and includes detailed explanations, comments, and Jupyter notebooks for easy experimentation.
To use any of the implemented algorithms, follow these steps:
-
Clone the repository:
git clone https://github.com/devProAbhi/MLcodefromScratch.git
-
Navigate to the specific algorithm directory you're interested in.
-
Open the Jupyter notebook or Python script associated with the algorithm.
-
Follow the provided instructions and examples to run and experiment with the algorithm.
Contributions to this project are welcome! If you'd like to contribute an implementation of a machine learning algorithm, fix a bug, or improve documentation, please follow the guidelines in the CONTRIBUTING.md file.