Machine Learning Tutorial 0/98 lessons ~6 min read Lesson 3
Types of Machine Learning
Types of Machine Learning: ML is mainly divided into three core types — supervised, unsupervised, and reinforcement learning.
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Focus
5 guided sections
Practice signal
Examples included
Career prep
Foundation builder
Introduction
Types of Machine Learning: ML is mainly divided into three core types — supervised, unsupervised, and reinforcement learning. Two additional types (self-supervised and semi-supervised) have become increasingly important in real-world applications, especially in deep learning.
Understanding the topic
Core types:
- Supervised Learning — trains on labeled data to predict or classify new, unseen data.
- Unsupervised Learning — finds patterns or groups in unlabeled data (clustering, dimensionality reduction).
- Reinforcement Learning — learns through trial and error to maximize rewards, ideal for decision-making.
- Self-Supervised Learning — generates its own labels from data; powers large-scale pretraining.
- Semi-Supervised Learning — combines small labeled data with large unlabeled sets.
| Type | Data | Goal | Examples |
|---|---|---|---|
| Supervised | Labeled | Predict class or value | Spam detection, house prices |
| Unsupervised | Unlabeled | Find structure | Customer segments, PCA |
| Reinforcement | Rewards | Optimal actions | Game AI, robotics |
| Semi-supervised | Mix | Learn with few labels | Medical imaging |
Execution workflow
1Choosing the right type
1 / 4Have labels?
Yes → supervised (classification or regression).
Best practices
- Most tabular business problems start with supervised learning.
- Use unsupervised EDA even when labels exist — it reveals data quality issues.
Summary
Pick supervised for labeled prediction, unsupervised for structure, reinforcement for sequential decisions.
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