This repository contains implementations of various machine learning algorithms from scratch. Each algorithm is implemented in Python and is contained in its own directory.
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K-Nearest Neighbors (KNN): The KNN algorithm assumes that similar things exist in close proximity. In other words, similar things are near to each other. Navigate to implementation
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Logistic Regression: Logistic Regression is a Machine Learning algorithm which is used for the classification problems, it is a predictive analysis algorithm and based on the concept of probability. Navigate to implementation
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Mean Squared Error Linear Regression (MSELinearRegression): Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. Navigate to implementation
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Neural Networks: Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. Navigate to implementation
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Support Vector Machines (SVM): Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning. Navigate to implementation
Each algorithm is implemented in a standalone Python file and can be run independently. For example, to use the KNN implementation, navigate to the KNN directory and import the KNN.py file.
Contributions are welcome. Please submit a pull request if you have something to add or improve.
This project is licensed under the MIT License.