Types of ML#

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Supervised Learning#

supervised-learning

In supervised learning, the model is trained on a labeled dataset, meaning each training example is paired with an output label. The goal is to learn a mapping from inputs to outputs so that the model can predict labels for unseen data.

Unsupervised learning#

unsupervised-learning

Unsupervised learning involves training a model on data that does not have labeled responses. The goal is to discover underlying patterns or structures in the data, such as grouping similar items together.

Examples of unsupervised learning tasks:

  • clustering

  • dimension reduction

  • discovering latent factors

  • searching for association rules

Semisupervised learning#

semisupervised-learning

Semi-supervised learning comes into play when you have a dataset that contains both labeled and unlabeled data. Semi-supervised learning is often used in scenarios where obtaining labeled data is expensive, time-consuming, or otherwise challenging.

Reinforcement learning#

https://lilianweng.github.io/posts/2018-02-19-rl-overview/RL_illustration.png

Reinforcement learning is a machine learning paradigm where an agent learns to make sequential decisions by interacting with an environment. It aims to maximize a cumulative reward signal by exploring actions and learning optimal strategies through trial and error.