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In supervised learning, the data being analyzed is labeled to show the machine what patterns it should be looking for. Think of it like a sniffer dog that locates targeted objects once it knows what scent it is after. That is exactly what happens when you play Netflix shows; you are simply telling the system's algorithms to find and display similar shows based on your favorite genre or what you have been recently watching. After a few logins into Netflix, the system can guess and provide a series of shows based on your previous inputs. It can also compare the output with the intended output to see if there are errors in it and rectify them accordingly.
Unlike supervised learning, in this algorithm, data is not labeled. The machine just searches for whatever patterns it can locate in the data. It’s like having a dog sniff into many different objects and arranging them in groups or clusters with similar smells. Unsupervised learning is not as popular as supervised learning because its applications are less obvious. Surprisingly, it has gained lots of traction in cybersecurity. Unsupervised learning does not figure out the correct output but it can analyze data and draw inferences to identify hidden structures and patterns from unlabeled data.
The semi-supervised algorithms are a hybrid of supervised and unsupervised learning because they train using both labeled and unlabeled data. It is a method that uses a small labeled data sample and a big sample of data that is not labeled to train the systems. Semi-supervised learning algorithms are usually chosen if the data labels require relevant and skilled resources to train or learn from the system. The labeled data is utilized in training a machine learning model that is then used to label unlabeled data.
This is the latest invention of machine learning algorithms. Unlike the ones we have discussed above, this one learns through trial and error to meet a specified objective. Reinforcement models try many different things and are awarded for achieving an objective or penalized if their behavior prevents them from meeting an objective. A good example of reinforcement learning is Google’s Alpha Go, a program that is popularly known for beating the most skilled human players in the complex Go game.