Machine learning (ML) is the application of artificial intelligence and the study of statistical models and algorithms that computer systems use to gradually improve their performance on specific tasks. In other words, it gives systems the ability to learn automatically and improve from experience without needing to be programmed for particular tasks.

A couple of examples of machine learning that you may have already heard about are the email spam filter or Youtube recommendation engine suggesting videos based on your viewing history and habits.

ML Categories

There are five broad categories that all machine learning algorithms fall into according
to how much human supervision is required to properly train them.

Reinforcement learning

It happens when a computer system is supplied with data in a specific environment and learns how to maximize its outcomes. It is mostly used in research, but there are currently many cases of industry use being reported. One example of this technique is AlphaGo, a computer program playing Go, the board game, developed by Google DeepMind.

Transfer learning

It involves the application of one model which was trained while solving one problem and then applying it to another problem, which is different but related. For example, gained knowledge while learning to recognize cars can also be applied when attempting to identify other kinds of vehicles, like trucks.

Supervised learning

A task of learning a function that maps an input to an output according to given examples of input-output pairs provided by the data scientist. These kinds of algorithms are commonly used in database marketing, handwriting recognition, information retrieval and extraction, spam detection, pattern and speech recognition, etc.

Unsupervised learning

It has no human guidance, and the system attempts to recognize patterns from unclassified, uncategorized, raw data. The main premise is that the commonalities in the data should be identified and a reaction given based on the absence or presence of such commonalities in each new data piece. One of the most common applications of unsupervised learning is anomaly detection, like preventing identity theft by discovering patterns associated with fraudulent credit card transactions.

Semi-supervised learning

This is a mix of the previous two approaches. It makes use of a large amount of unlabeled data and a small amount of labeled data. Research results suggest that when unlabeled data is used with a small amount of labeled data there is a more significant improvement in learning accuracy than unsupervised learning (unlabeled data only). Best-known utilizations of the semi-supervised learning approach are facial recognition algorithms in Google and Facebook photo services.

Given that machine learning enables analyzing enormous quantities of data, delivering fast and accurate results, it is safe to say that its application in processing vast volumes of information is undoubtedly going to grow even more in the future.

All You Need to Know about Machine Learning

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