Explain different types of Machine Learning with a diagram.
Types of Machine Learning
1. Supervised Learning
In supervised learning, the model is trained on a labeled dataset, meaning each input has a known output. The goal is to learn a mapping from inputs to outputs.
Subtypes:
- Classification: Predicts discrete labels or categories.
Example: Email spam detection, handwriting recognition. - Regression: Predicts continuous numerical values.
Example: Predicting house prices, stock market trends.
2. Unsupervised Learning
In unsupervised learning, the model works with unlabeled data. It tries to identify hidden patterns or structures in the data without any predefined output.
Subtypes:
- Cluster Analysis: Groups data points into clusters based on similarity.
Example: Customer segmentation. - Association Mining: Finds relationships or rules among variables.
Example: Market basket analysis (if a person buys bread, they are likely to buy butter). - Dimension Reduction: Reduces the number of variables while preserving essential information.
Example: PCA (Principal Component Analysis).
3. Semi-Supervised Learning
This type lies between supervised and unsupervised learning. It uses a small amount of labeled data along with a large amount of unlabeled data.
Use Case: Useful when labeling data is expensive or time-consuming (e.g., in medical imaging).
4. Reinforcement Learning
In reinforcement learning, an agent learns by interacting with an environment. It receives rewards or penalties based on its actions and aims to maximize the cumulative reward over time.
Applications:
- Game playing (e.g., AlphaGo)
- Autonomous driving
- Industrial robotics