Types of Machine Learning
Supervised Learning
Supervised learning is a machine learning technique in which the model is trained on a labeled training set.
The most popular supervised learning tasks are:
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Regression (for example, predicting the price of a house): you will need a training set labeled with other house prices for that;
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Classification (for example, classifying email as spam/ham): you will need a training set labeled as spam/ham for that.
Unsupervised Learning
Unsupervised learning is a machine learning technique in which the model is trained on an unlabeled training set.
The main unsupervised learning tasks are clustering, anomaly detection, and dimensionality reduction.
Clusterization
Groups similar data points into clusters without labels β for example, grouping emails without knowing whether they are spam or not.
Anomaly Detection
Finds data points that deviate from normal patterns, such as unusual credit card transactions, without needing fraud labels.
Dimensionality Reduction
Reduces the number of features while preserving important information β also label-free.
Reinforcement Learning
Reinforcement learning differs significantly from the previous two types. It is a technique used to train self-driving vehicles, robots, AI in gaming, and more.
Reinforcement learning is a machine learning technique in which the agent (e.g., vacuum cleaner robot) learns by making decisions and getting a reward if the decision is correct and a penalty if the decision is wrong.
Training a dog to fetch works similarly to reinforcement learning: good actions earn a reward, wrong actions earn a penalty, and successfully bringing the ball earns a larger reward, reinforcing the desired behavior.
1. To train the ML model for a supervised learning task, you need a training set to contain target (be labeled). Is it correct?
2. To train the ML model for a unsupervised learning task, containing a target (being labeled) for a training set is not required. Is it correct?
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Supervised Learning
Supervised learning is a machine learning technique in which the model is trained on a labeled training set.
The most popular supervised learning tasks are:
-
Regression (for example, predicting the price of a house): you will need a training set labeled with other house prices for that;
-
Classification (for example, classifying email as spam/ham): you will need a training set labeled as spam/ham for that.
Unsupervised Learning
Unsupervised learning is a machine learning technique in which the model is trained on an unlabeled training set.
The main unsupervised learning tasks are clustering, anomaly detection, and dimensionality reduction.
Clusterization
Groups similar data points into clusters without labels β for example, grouping emails without knowing whether they are spam or not.
Anomaly Detection
Finds data points that deviate from normal patterns, such as unusual credit card transactions, without needing fraud labels.
Dimensionality Reduction
Reduces the number of features while preserving important information β also label-free.
Reinforcement Learning
Reinforcement learning differs significantly from the previous two types. It is a technique used to train self-driving vehicles, robots, AI in gaming, and more.
Reinforcement learning is a machine learning technique in which the agent (e.g., vacuum cleaner robot) learns by making decisions and getting a reward if the decision is correct and a penalty if the decision is wrong.
Training a dog to fetch works similarly to reinforcement learning: good actions earn a reward, wrong actions earn a penalty, and successfully bringing the ball earns a larger reward, reinforcing the desired behavior.
1. To train the ML model for a supervised learning task, you need a training set to contain target (be labeled). Is it correct?
2. To train the ML model for a unsupervised learning task, containing a target (being labeled) for a training set is not required. Is it correct?
Thanks for your feedback!
