3 Categories of Machine Learning - Deepstash

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3 Categories of Machine Learning

3 Categories of Machine Learning

  1. Supervised learning: It has a set of labelled data to train the model on. In a way, it means supervising a machine by providing a ton of information about a particular case and giving it the case outcome.
  2. Unsupervised learningUnsupervised learning relies on giving the machine or algorithm unlabeled data and allowing it to identify the pattern on its own.
  3. Reinforcement learning: It depends on ML algorithms with a set of rules and let it learn on its own on how to achieve the goals.

Narrow AI & General AI

Narrow AI & General AI

Very broadly, AI can be divided into two: narrow AI and general AI.

1. Narrow AI systems handle singular or limited tasks. Also referred to as weak AI sometimes, such systems have applications in email spam filtering, recommendation systems, and autonomous vehicles.

2. On the other hand, general AI or strong AI, refers to a machine’s capability to think and function as a human. It denotes the ability to distinctly recognise other intelligent entities’ needs, emotions, and thoughts.

Machine Learning as a part of a greater whole

Machine Learning as a part of a greater whole

Machine learning relies on defining behavioural rules by examining a set of data to find patterns. Since the main objective of ML is to enable machines to learn by themselves, given a set of data, it is merely a technique for realising AI.

To wrap it up...

To wrap it up...

  1. AI refers to machines exhibiting human-like intelligence. There are several techniques for that, machine learning being one of the most prominent ones.
  2. AI’s ultimate goal is to develop a smart system to simulate human thinking and intelligence, while ML allows the machine to learn from data to give the desired output.
  3. While AI aims to make machines more human-like, ML helps in making machines learn like humans.

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