Discover the Different Types of Machine Learning
Machine learning has been around for some time now and there are several different types in general. Therefore, it’s no mistery that there are all sorts of projects that put them to use.
In case you’re not familiar with machine learning, it’s a sort of artificial intelligence that lets computers make predictions. Furthermore, it does that without any explicit programming.
It involves training a model on a dataset, from which it’s going to find patterns and based on them make decisions and predictions. However, for accurate predictions it all depends on the structure of your model and the quality of your dataset.
There are 4 main types of machine learning, each with its own unique characteristics and suitable applications. Furthermore, we call these 4 types supervised, unsupervised, semi-supervised and reinforcement learning.
Types of Machine Learning
Supervised Learning
One of the most common and easy to understand type of machine learning is supervised learning. It involves training a model on a labeled data. In essence, we feed training examples to the model, where we tell it what goes in the input and what needs to come out at the output.
Models are basically complex equations and while training, its adjusting the variables inside. By doing so, it learns to output something close to what we defined with labels.
There are also different types of supervised learning, such as classification, regression, decision trees and support vector machines.
Unsupervised Learning
With this type of machine learning, we train our models without using a labeled dataset. In turn, the model needs to find patterns from data on its own.
Examples of unsupervised learning are clustering and dimensionality reduction.
Semi-supervised Learning
This one is a combination of the two we have already described. It involves training a model on a dataset that is partially labeled and partially unlabeled.
We use this type of machine learning when we’re dealing with a large amount of data, where only small amount of labeled data is available.
Reinforcement Learning
This type is quite different than the others we described above. In order to train our model, we create a so called agent, which learns to interact from its environment to maximize a reward.
When the agent interacts with the environment, it receives positive and negative feedback from it in the form of rewards and punishments. In essence, it learns through trial and error to optimize it’s actions in order to maximize rewards.
It’s been used in a variety of applications, such as autonomous vehicles and game AI.
Conclusion
In conclusion, there are four types of machine learning as we described them above. Furthermore, each of these types has its own unique characteristics and applications. Therefore, we can use them in a variety of industries and fields to boost and enhance our workflow.
I hope this article helped you gain a better understanding on machine learning in general. Furthermore, I hope it inspires you to research even more about it and each of its types.
