Types of Machine Learning
Introduction
This artifact introduces the three major types of machine learning - supervised, unsupervised, and reinforcement learning—outlining their mechanisms, differences, and real-world applications.
Description
Machine learning is a subfield of artificial intelligence (AI) that focuses on developing algorithms that can learn from data. This project explains the core distinctions among supervised, unsupervised, and reinforcement learning, using visual aids and practical examples from the provided resource.
Objective
To help learners understand the different learning paradigms in machine learning and how they apply to various real-world tasks such as prediction, clustering, and decision-making.
Process
- Research and review foundational literature on machine learning types.
- Extract key definitions and examples from the resource (Types of Machine Learning PDF).
- Visualize the categories and differences using a structured flowchart and table.
- Integrate graphical illustrations.
- Organize the artifact into clearly labeled educational sections.
Tools/Technologies Used
- Types of Machine Learning PDF (source material)
- Markdown for documentation
- Mermaid.js for flowchart visualization
- React for interactive components
- Next.js for web framework
- Tailwind CSS for styling
Value Proposition
This artifact delivers an accessible overview of ML types, combining conceptual clarity and visual learning aids. It helps learners distinguish among the three learning approaches, making it a valuable resource for beginners and educators alike.
References
- "Types of Machine Learning" PDF
Artifact Summary
Visual Taxonomy
Basic Comparison
| Category | Supervised Learning | Unsupervised Learning | Reinforcement Learning |
|---|---|---|---|
| Basic Definition | Learn from labeled data | Learn from unlabeled data | Learn by interacting with environment |
| Real-World Applications | Spam detection, house price prediction | Customer segmentation, anomaly detection | Autonomous driving, game playing |
| Common Algorithms | Linear Regression, SVM, Random Forest | K-Means, PCA, DBSCAN | Q-Learning, Deep Q Network (DQN), PPO |
Workflow Comparison
| Step | Supervised Learning | Unsupervised Learning | Reinforcement Learning |
|---|---|---|---|
| 1 | Collect labeled data | Gather unlabeled data | Define agent and environment |
| 2 | Preprocess (normalize, encode labels) | Scale features, remove noise | Define reward function |
| 3 | Choose regression/classification algorithm | Choose clustering/dimensionality reduction | Initialize agent policy |
| 4 | Train model on labeled data | Fit model to discover patterns | Agent explores environment |
| 5 | Evaluate on test set | Evaluate with internal metrics (e.g. Silhouette score) | Update policy based on reward |
| 6 | Use model to predict outcomes | Apply clusters or features to tasks | Deploy agent in real-world environment |