Question

Which of the following is NOT a category of Machine Learning Algorithms?Deep Learning
Reinforcement
Supervised
Unsupervised

Answer 1

Deep learning is not a category.

• There are four categories namely -

1. Reinforcement
2. Supervised
3. Semi-supervised
4. Unsupervised

• Supervised is the simplest of all categories, and they involve the model user's close oversight.
• The developer has no straight control over unsupervised learning methods.
• Semi acts as a ground among supervised and unsupervised
• The mechanics of reinforcement are straightforward: an action occurs, the repercussions are seen, and the following action takes into account the outcomes of the previous action.

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Answer 2

Deep learning is not a category of machine learning algorithms.

Four categories of machine learning :

Supervised learning

• The machine is taught by example in supervised learning.
• The operator gives the machine learning algorithm a known dataset including desired inputs and outputs, and the system must figure out how to get to those inputs and outputs.

Semi-supervised learning

• Semi-supervised learning is similar to supervised learning, except it incorporates both labeled and unlabeled data.
• Labeled data is information that has meaningful tags so that the algorithm can interpret it, whereas unlabeled data does not have those tags.

Unsupervised learning

• The machine learning algorithm looks for patterns in data.
• There is no answer key or human operator available to provide assistance.
• Instead, the machine analyses available data to discover correlations and linkages.
• The machine learning algorithm is left to evaluate enormous data sets and respond to them in an unsupervised learning process.

Reinforcement learning

• Reinforcement learning is concerned with structured learning processes in which a machine learning algorithm is given a set of actions, parameters, and end values to work with.
• The machine learning algorithm attempts to explore several options and possibilities after defining the rules, monitoring and assessing each output to determine which is the best.
• Reinforcement learning instructs the system to learn through trial and error.

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