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Choosing the Right Approach
Knowing which type of machine learning to use for a given problem is a key skill. Here is a practical decision framework.
10 min•By Priygop Team•Updated 2026
Decision Framework
Before choosing an approach, ask these questions:
- 1Do I have labeled data? If yes: consider supervised learning. If no: consider unsupervised.
- 2What type of output do I need? A category (spam/not spam)? Use classification. A number (house price)? Use regression.
- 3What type of input is it? Tabular data: classical ML. Images, audio, text: deep learning.
- 4How much data do I have? A few thousand examples: classical ML. Millions of examples: consider deep learning.
- 5Do I need to explain my decisions? Yes: use simpler, interpretable models. No: consider deep learning.
Diagram
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Deep Learning ⊂ Machine Learning ⊂ Artificial Intelligence
Quick Decision Guide
- Predict a yes/no outcome from a spreadsheet -> Supervised classification (logistic regression, decision tree)
- Predict a number from a spreadsheet -> Supervised regression (linear regression, gradient boosting)
- Group customers with no labels -> Unsupervised clustering (K-Means)
- Recognize objects in photos -> Deep learning (CNN)
- Understand and generate text -> Deep learning (Transformer)
- Train an agent to play a game -> Reinforcement learning
Key Takeaways from This Module
- Machine Learning is a type of AI where computers learn patterns from data
- The three types are: supervised (labeled data), unsupervised (no labels), and reinforcement (rewards)
- Deep Learning uses multi-layer neural networks and is best for images, audio, and text
- AI is the broadest category. ML is inside AI. Deep Learning is inside ML
- Choose classical ML for tabular data and smaller datasets
- Choose deep learning for images, audio, video, and large text datasets
- For deeper ML knowledge, the Machine Learning course on PriyGop covers algorithms in detail
Key Takeaways
- Knowing which type of machine learning to use for a given problem is a key skill.
- Predict a yes/no outcome from a spreadsheet -> Supervised classification (logistic regression, decision tree)
- Predict a number from a spreadsheet -> Supervised regression (linear regression, gradient boosting)
- Group customers with no labels -> Unsupervised clustering (K-Means)