Welcome to the Machine Learning Repository! This repository is dedicated to exploring the fascinating field of machine learning and its various algorithms.
Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data. It encompasses a wide range of techniques and methodologies, including supervised learning, unsupervised learning, reinforcement learning, and deep learning.
This repository covers a variety of machine learning algorithms, including but not limited to:
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- k-Nearest Neighbors (KNN)
- Naive Bayes
- Artificial Neural Networks (ANN)
- and many more!
In addition to exploring machine learning algorithms, this repository contains applications of these algorithms over various datasets. These datasets cover diverse domains such as healthcare, finance, marketing, and more. By applying machine learning algorithms to real-world datasets, we aim to gain insights, make predictions, and solve practical problems.
Feel free to explore the code, datasets, and documentation in this repository. You can use the provided notebooks or scripts to understand, experiment with, and apply machine learning algorithms to your own projects or datasets.
Contributions are welcome! If you have suggestions, improvements, or want to add your own implementations of machine learning algorithms or datasets, please feel free to submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.