The concept of machine learning is completely changing the world and revolutionizing various sectors. But did you know that there are different types of machine learning as well? The concept behind it is to ensure that systems can learn and improve data without the need for programming. 

Its algorithms are quite varied, and each caters to a different segment. There are five broad categories, and each is unique. Having an idea about the different types will help you understand their applications. 

The Five Broad Types of Machine Learning

The five broad categories that you should know about are:

1. Supervised

The first and most common type is supervised machine learning. In this approach, the model works on labeled data. What does this mean? 

It implies that each input data point is associated with a corresponding target label. The focus of this type of learning is to ensure that mapping from inputs to outputs is accurate even if the data is not seen. 

Some of the very common types of algorithms that you will find in supervised learning are linear regression, logistic regression, and even decision trees. Even the Naive Bayes classifiers are very common during the formulation of supervised machine learning. 

Are you wondering where you can find the application of supervised learning? It is most commonly used in domains like risk assessment, image recognition, and predictive analysis. Supervised learning can also come in handy extensively for fraud detection. The good thing about supervised machine learning is that its high efficacy rate ensures it can be used across various fields. 

2. Unsupervised

Now that we know about the concept of supervised machine learning and its application, another quite popular machine learning type is the unsupervised one. 

Unlike supervised learning, unsupervised learning is trained with unlabeled data. The model is aimed at finding data or even patterns without any prior guidance. Some of the common unsupervised machine learning algorithms include Apriori, Gaussian Mixture Models, and even Principal Component Analysis. 

A very common type of unsupervised machine learning is cluster analysis—clustering algorithms, such as K-means and hierarchical clustering, group similar data points together. 

This is usually based on the features. Even probabilistic clustering is quite a popular method used in the unsupervised machine learning segment. The application of unsupervised machine learning is usually the highest in sectors like customer segmentation and even the detection of any anomalies. 

You must have seen the prompt on most e-commerce websites like “customers who have bought this also bought……”. It is usually unsupervised machine learning, which helps one detect it. The basic concept behind supervised machine learning is that it is based on likelihood and grouping similar data or patterns. 

3. Self Supervised

Yes, you read it right. Self-supervised learning is also quite effective and widely prevalent. Here, you do not need massive sets of data, and it allows models to train themselves on unlabelled data. This is a comparatively newer form of machine learning, and it does not require any kind of explicit supervision. 

Unlike supervised learning, where external sources provide labels, self-supervised learning generates labels automatically from the input data. How does it work? 

One of the most common instances where self-supervised learning is great is in predicting parts of missing images. It is also capable of generating relevant image representations. 

The SSL algorithms are predictive, making them a great fit for natural language processing (NLP), computer vision, and speech recognition. 

4. Reinforcement

Just like humans, you can even train machines based on reinforcement. Just like the principle of reinforcement, you will train data based on punishment and rewards. In this form of machine learning, the agent usually learns how to interact with an environment. They usually learn this to achieve a goal by taking action and then giving feedback, usually either in the form of reward or punishment. 

The good thing about reinforcement learning is that, contrary to supervised learning, it does not only depend on labeled data. Even learning through trial and error is given maximum importance. The agent will make sure to explore the environment and then garner learnings from the same as well. 

It will then adjust the behavior so that one can maximize the cumulative rewards. Some of the most popular algorithms that are often used for reinforcement learning include learning, policy gradients, and deep Q-networks (DQN). Many fields have particularly benefited from reinforcement learning. Some of the most common ones include the gaming sector, robotics, and even autonomous vehicles. Reinforcement learning is extensively used in robotics, where it teaches the robot how to replicate the human task with efficacy. 

5. Semi-Supervised

Finally, another very important type of machine learning that you should know about is the semi-supervised learning method. This method is named so because it has elements of both supervised and unsupervised learning. 

This type of learning usually uses a small amount of labeled data along with a more extensive pool of unlabeled data. It helps improve model performance. You will usually notice that a semi-supervised learning model might use unsupervised learning to identify data clusters. 

Again, it might also use supervised learning to label the clusters. It is like the perfect amalgamation of the two concepts of supervised and unsupervised learning. One common technique in semi-supervised learning is self-training. In this case, a model that was originally trained on labeled data is used to generate pseudo levels for unlimited data. A very common field where this form of learning can be put to good use is speech recognition. 

Conclusion

Machine learning is the future of technology, and without the right kind of machine learning, the output will not be impressive at all. Each of the different types of machine learning that we have spoken about is not only different in structure, but also the objectives are different. If you are someone who deals with artificial intelligence and machine learning, then you will come across all the different types. As the concept of machine learning continues to evolve, the types will also improve further. Mixing and matching different kinds of methodologies can help you to garner the most precise response.