Machine Learning (ML) can often sound intimidating, but for product managers, it’s less about math and more about selecting the right tool for the right problem. Let’s break down the main types of ML algorithms in simple terms—and when you should use each.
1. Supervised Learning
What it is:
You train the model using historical data where the correct answer is already known.
Common use cases:
Predicting prices, demand, or churn
Fraud detection
Credit scoring or risk prediction
Examples:
Linear Regression, Logistic Regression, and Decision Trees.
When PMs should use it:
Use supervised learning when you have past data with labels (e.g., customer bought/didn’t buy, policy lapsed/active) and want to predict a clear outcome.
2. Unsupervised Learning
What it is:
The model finds patterns in data without predefined labels.
Common use cases:
Customer segmentation
Identifying hidden patterns in usage behavior
Grouping similar products or users
Examples:
Clustering (K-Means), Principal Component Analysis (PCA)
When PMs should use it:
Perfect when you’re in exploration mode—trying to understand users better or discover segments you didn’t know existed.
3. Semi-Supervised Learning
What it is:
A mix of labeled and unlabeled data.
Common use cases:
When labeling data is expensive or slow
Image or document classification
When PMs should use it:
Use this when you have limited labeled data but lots of raw data and still want meaningful predictions.
4. Reinforcement Learning
What it is:
The model learns by trial and error, using rewards and penalties.
Common use cases:
Recommendation systems
Dynamic pricing
Personalization engines
When PMs should use it:
Best for continuous optimization problems where the system learns over time based on user actions.
Final PM Takeaway
Don’t start with algorithms, start with the product question:
Predict something → Supervised
Discover patterns → Unsupervised
Optimize continuously → Reinforcement
Machine learning works best when it solves a real product problem, not when it’s added just because it sounds cool.