types of machine learning


 Machine learning types is a new trend field nowadays and is an application of artificial intelligence. It uses certain statistical algorithms to make computers work in a certain way without being explicitly programmed. The algorithms receive input value and predict output by using certain statistical methods. 


The main purpose of machine learning is to create intelligent machines that can think and work like humans.


Types of machine learning

Reinforcement Learning

Reinforcement learning is quite different compared to supervised and unsupervised learning. Where we can easily see the connection between unsupervised supervision (presence or absence of labels), the connection to learning reinforcement is a little murkier. 


Some people try to tie reinforcement learning closer to two by describing it as a kind of learning that relies on a sequence of time-dependent labels however my opinion is that it just makes things more confusing.


I prefer to look at reinforcement learning as learning from mistakes. Set up a reinforcement learning algorithm in any environment and it will make a lot of mistakes in the beginning. 


As long as we provide some signal to the algorithm that associates good behaviors with positive signals and bad behaviors with negative we can strengthen our algorithm to prefer good behaviors over bad behaviors. Overtime types of machine learning algorithm models learn to make fewer mistakes than before.


Learning reinforcement greatly prevents behavior. It has influences from the fields of neuroscience and psychology. If you've heard of Pavlov's dog you may already be familiar with the idea of ​​strengthening an agent albeit a biological one.


However to truly understand the degree of reinforcement let’s detail a concrete example. Let's consider teaching an agent to play the Mario game.


For any reinforcement learning problem, we need an agent and environment as well as a way to connect the two through a feedback loop. To connect the agent to the environment we give him a set of actions he can perform that affect the environment. 


To connect the environment to the agent we have to constantly issue two signals to the agent: updated status and reward (our reinforcement signal for behavior).


In Mario's game, our agent is our learning algorithm and our environment is the game (probably a certain level). Our agent has a set of actions. These will be our button modes. Our updated status will be each game frame as time goes on and our reward signal will be the score change. 


As long as we connect all models of these types of elements a machine learning scenario is reinforced to reinforce playing the Mario game.


Supervised learning

Supervised learning is the most popular paradigm for the types of machine learning. It is the easiest to understand and the simplest to implement. It is very similar to teaching a child using flashcards.


Given data in the form of samples with labels, we can enter a learning algorithm of pairs of sample labels one after the other allowing the algorithm to predict the label for each sample and give it feedback on whether it predicts the correct answer or not. Over time the algorithm will learn up close the exact nature of the relationship between examples and their labels. 


When fully trained the supervised machine learning algorithm will be able to observe a new data example that has not been seen before and predict good label types for it.


Supervised machine learning is often described as task-oriented because of its types. It is very focused on a single task and feeds more and more examples into an algorithm until it can perform the same task. This is the kind of learning you will probably encounter.


Unsupervised Learning

Unsupervised learning is very much the opposite of supervised learning. It does not include labels. Instead, our algorithm will enter a lot of data and get the tools to understand the properties of the data. 


From there he can learn to group and/or organize the data in such a way that a person (or other smart algorithms) can log in and understand the newly organized data.


What makes unsupervised machine learning such an interesting area is that an overwhelming majority of the types of data in this world is untagged. 


The existence of smart algorithms that can take our terabytes and terabytes of unlabeled data and understand them is a huge source of potential profit for many industries. This alone can help increase productivity in several areas.


For example, what if we had large database types of every research ever published and we had unsupervised machine learning algorithms models that knew how to group them in such a way that you were always aware of current progress in a particular field of research. 


Now you start your own research project and integrate your work into this network that the algorithm can see. 


As you write your work and take notes the algorithm offers you suggestions for related works you may want to quote and works that may even help you push this field of research forward. With the help of such a tool, you can greatly increase your productivity.


Because unsupervised learning is based on data types and its features we can say that unsupervised machine learning is based on data. The results of an unsupervised learning task are controlled by the data and how they are formatted.

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