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 most popular unsupervised learning tasks are clusterizaion, anomaly detection, and dimensionality reduction.
Clusterization
It is a process of grouping similar data points into clusters. You do not need to label the data for it. For example, a training set of emails without labels spam/ham will do.
Anomaly Detection
It is a process of detecting deviations from normal data behavior. For example, fraud detection in credit card transactions. No need to label fraud/not fraud. Simply give the transaction information to a model, which will determine if the transaction stands out.
Dimensionality Reduction
It is a process of reducing the number of dimensions while retaining as much relevant information as possible. It also does not require any labels.
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.
Imagine training a dog to fetch a ball. The dog would receive a reward (such as a treat or praise) for picking up the ball and bringing it closer to the owner. It would receive a penalty (such as withholding the treat or a disappointed tone) if it runs in the wrong direction or gets distracted. Additionally, it would get a large reward once it successfully retrieves the ball and delivers it to the owner.
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 most popular unsupervised learning tasks are clusterizaion, anomaly detection, and dimensionality reduction.
Clusterization
It is a process of grouping similar data points into clusters. You do not need to label the data for it. For example, a training set of emails without labels spam/ham will do.
Anomaly Detection
It is a process of detecting deviations from normal data behavior. For example, fraud detection in credit card transactions. No need to label fraud/not fraud. Simply give the transaction information to a model, which will determine if the transaction stands out.
Dimensionality Reduction
It is a process of reducing the number of dimensions while retaining as much relevant information as possible. It also does not require any labels.
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.
Imagine training a dog to fetch a ball. The dog would receive a reward (such as a treat or praise) for picking up the ball and bringing it closer to the owner. It would receive a penalty (such as withholding the treat or a disappointed tone) if it runs in the wrong direction or gets distracted. Additionally, it would get a large reward once it successfully retrieves the ball and delivers it to the owner.
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!
