Types of Machine Learning
1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning
1. Supervised Learning
In supervised learning, the machines are made to learn by feeding large amounts of data in it. At the time of training, for example, these systems are filled with large amounts of data of handwritten figures to indicate the right answers.
If they are packed with a sufficient amount of data, they can easily learn to judge different clusters of pixels and shapes associated with each number.
As a result of which they can easily be able to recognize handwritten numbers and be able to differentiate between 4 and 8 or so. However, building a supervised machine learning system requires a lot of data in millions to give the best output as a result. Therefore, the datasets that are used to train these systems are extremely vast.
Some of the examples are like Google’s Open Images Dataset that has around 9 million images, the next is YouTube-8M videos suggestions that has around 7 million labeled videos to suggest each category or ImageNet that has around 14 million categorized images.
The most interesting part of these datasets is that they continuously keep growing, for example, Facebook has announced recently that it has assembled around 3.5 billion images available on Instagram. These one billion photos are responsible for making the Facebook environment work efficiently and provide you the right suggestion through its image recognition system.
2. Unsupervised Learning
In unsupervised learning, the machines are capable of learning with their past experiences through certain algorithms. Here, they try to find patterns in data and spot out the similarities and then divide them into different categories.
The clustering of different houses available on rent under any website or grouping of the same stories together in Google News are the best examples of Unsupervised Learning. The algorithms in unsupervised learning are not designed to give the output based on any specific data types. They simply learn from their past experiences and then they give the output based on similarities or differences.
Semi-Supervised Learning
As the name suggests, the semi-supervised learning is the combination of both supervised and unsupervised learning. It involves the technique of using a small amount of labeled and a large amount of unlabeled data to train the systems. In semi-supervised learning, initially, the labeled data is used to train the machine model, and then this model is used to label all the unlabeled data.
This process is known as pseudo-labeling. This model then automatically gets trained with the combination of both labeled and unlabeled data.
In recent years, the usage of semi-supervised learning has increased incredibly high by the Generative Adversarial Networks (GANs).
These are the machine learning systems that can generate new data from the past labeled data. The example for this is creating new images of Pokemon from existing ones which in return helps train a machine learning model.
If by any means, semi-supervised learning can become as successful as supervised learning, the usage of huge amounts of computer power may end up. As a result, machine learning systems can be trained successfully without giving much access to a huge amount of labeled datasets.
3. Reinforcement Learning
Reinforcement Learning means whenever the machine gives any output, it is simply awarded or punished based on the result. Let us try to understand reinforcement learning with the help of a very simple example.
The technique of reinforcement learning is similar to a young boy who wants to play computer games for the first time. Initially, he must be not familiar with the rules and controls of the game.
But after playing for a certain limit, he starts learning all the rules of the game and relation between the controls. As a result, he scores more and more, and his performance upgrades every time he plays.
The best example of reinforcement machine learning is Google DeepMind’s Deep Q-network. It is one of the hardest video games which has beaten many users because of its learning ability. Here, the machine is fed with various pixels and then it decides the information about every state of the game.
It then gives the score based on the state of the game and the action it performs. The more number of processes the machine completes through playing, the more advanced it will become, and it will be then more difficult to beat the machine.
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