Unsupervised learning

The distinction in unsupervised learning from supervised learning, as it may have surmised the data will not contain any labels, and the module should attempt prospects and learn without a teacher.

Semi-supervised learning

The semi-supervised learning algorithm is a combination of supervised learning and unsupervised. The algorithms run individually. For example, start with creating a module that begins gathering unlabeled data that will be later processed and labelled. It can go to another module that is on supervised learning mode waiting for labelled data.

3. giving unlabelled dataset to the algorithm.

Supposing we have random data of a specific amount of people (Figure 03), The Algorithm will find the connection between each person in the data provided even though it is not labelled (Figure 04).

4. The Algorithm Is learning about the dataset and classing it. ("Clustering" in a future section)

Reinforcement learning

Reinforcement learning is different from other ways of learning. in this method used is called the agent, this agent will analyse the environment (dataset) and then it selects and performs actions, these actions if they are right, the agent get a reward, and the learning process keeps going. (Figure 05)

5. graph illustrates reinforcement model.
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