Welcome to the Machine Learning Mastery repository. This repository is designed to provide practical and comprehensible examples of various algorithms and methods used in the field of machine learning.
Machine learning is a powerful subset of artificial intelligence (AI) that focuses on developing algorithms and models that enable computers to learn from and make predictions or decisions based on data. Unlike traditional programming, where explicit instructions are provided, machine learning algorithms identify patterns and insights from data to make data-driven decisions with minimal human intervention. This approach is revolutionizing various industries, from healthcare and finance to marketing and beyond, by enabling the automation of complex tasks and the creation of intelligent systems.
Supervised learning is the most common and well-understood type of machine learning. In supervised learning, algorithms are trained using labeled data, meaning the input data is paired with the correct output. The goal is for the model to learn the mapping from inputs to outputs so it can make accurate predictions on new, unseen data.
- Applications: Regression and classification tasks, such as predicting house prices, diagnosing diseases, spam detection, and more.
- Example Algorithms: Linear Regression, Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forests, and Neural Networks.
Unsupervised learning algorithms work with unlabeled data. The goal is to identify hidden patterns or intrinsic structures in the input data without any specific output labels. This type of learning is particularly useful for exploratory data analysis, where the aim is to discover the underlying structure of the data.
- Applications: Clustering, association, and dimensionality reduction tasks, such as customer segmentation, market basket analysis, and reducing the dimensionality of data for visualization.
- Example Algorithms: K-Means Clustering, Hierarchical Clustering, DBSCAN, Principal Component Analysis (PCA), and Independent Component Analysis (ICA).
Reinforcement learning involves training an agent to make a sequence of decisions by interacting with an environment. The agent receives rewards or penalties based on its actions and learns to maximize the cumulative reward over time. This type of learning is inspired by behavioral psychology and is well-suited for tasks that require a long-term strategy.
- Applications: Game playing, robotic control, autonomous driving, and real-time decision-making tasks.
- Example Algorithms: Q-Learning, Deep Q-Networks (DQN), Policy Gradient methods, and Actor-Critic methods.
Contributions are welcome! Feel free to open an issue or submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
For questions or feedback, please reach out to Álvaro García Vásquez.