Thread by @gusthema

Machine Learning models can be classified regarding how much human supervision they need.

This affects the algorithms used and the types of tasks that it can solve.

You can categorize Machine Learning models in 4 major categories:

1/5🧵 #ML #MondayMotivation

Supervised Learning is when you train a model from the input data and ALL their corresponding labels.

Examples of
- Tasks: classification and regression
- Algorithms: kNN, Linear and Logistic regression, SVM, Decision Tree, Neural Networks(*)

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Unsupervised Learning is when you use unlabelled data to train your model.

Examples of
- Tasks: Clustering, Anomaly Detection, Visualization and Dimension reduction, Association rule
- Algorithms: K-means, PCA, DBSCAN

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Semi-supervised learning is used when you have labeled data but the labeling is incomplete and it's too expensive to label all of it.

In this case, the solution is a combination of supervised and unsupervised algorithms.

Generative 🔢➡️🖼️ models fit this category.

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Reinforcement learning 🤖🧠🕹️ is a family of algorithms that learn a policy that maximizes a reward when applied to an environment

Imagine a video game, the policy is the strategy to maximize the number of points when playing the game.

Some Algorithms: DQN, Actor-Critic

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