The supervised machine learning approach utilizes a lot of examples. Algorithms achieve this by exposing systems to vast amounts of labelled data.
Consider annotated images of handwritten figures as an example. Here, the images correspond with the numbers. Because of this, supervised learning algorithm/software can determine the written numbers.
The key takeaway is that supervised learning requires a large number of labelled data sets. In fact, you may have to expose the systems' algorithms to millions of examples to achieve consistency and reliability.
Unsupervised learning task attempts to identify resemblances that divide the data. For example, each day Google News clusters stories on similar topics. Airbnb also uses unsupervised learning in clustering available houses.
The engineers don't design such algorithms to single out specific data. The learning attempts to identify data that can be clustered. It uses similar attributes in this process. It can also recognize anomalies that separate the data sets.
The semi-supervised learning models combines both processes. It uses limited labelled data. The amount of unlabeled data in this learning is vast.
In semi-learning, the engineers perform a procedure called pseudo-labelling. They apply the labelled data in partial training of the ML model and use the model to label the unlabeled data. They train the algorithm model on the labeled and pseudo-labeled data.
Vast sets of labeled data may become less important over time. Many industries are now using semi-supervised machine learning.
Reinforced machine learning is easier to understand. Think about how you learned a particular skill, for example playing a computer game well.
At first, you were a complete novice that didn't understand the rules of the game. However, by playing the game frequently your performance got better with time. That's because you continuously looked at how the buttons related to each other, each moment. The continuous happenings on the screen and your in-game experiences reinforced your skills.
Reinforcement learning works in the same way. For example, Google Deep Q-network uses a reinforcement machine learning model. Its algorithm has now surpassed the human mind when playing vintage video games.
Just as in the Google deep Q-network example, data researchers feed the system with pixels from each game. They then determine different information concerning the game's status—for instance, the distance from one object to another on the screen. The system then learns how its actions relate to its score. Repeated cycles of playing enhance the creation of the machine learning models. Such models can determine which actions will reduce the score.
Consider the video game of Breakout. The machine learning software will recognize where the paddle should move. That way, it will intercept the ball.
In more real-world applications, reinforcement learning can be used for anything from teaching robots in a manufacturing plant how to take one of many identical objects out of a box, to managing electricity grids more safely and efficiently, or even improving investment decisions for large hedge funds.