If you are new to Machine Learning and want to learn from the basics, feel free to hop on to our article on What is Machine Learning and why is it important? which will make you familiar with the basics of this field.
Machine learning is the study of computer programs used for developing algorithms which can analyse data on their own, and learn automatically through experience.
But moreover Machine learning is about creating applications with theoretical and mathematical modelling.
There are three types of machine learning models broadly classified as:
- Supervised learning
- Unsupervised Learning
- Reinforcement Learning
In supervised learning, you’ll learn how to perform algorithms for mapping input to output functions. The learning should be in a way that when new data enters the system, the output is predicted by looking at the input.
Take this as an example of a teacher supervised learning, just that in this case algorithms learn from the training dataset. Algorithms learn to tell the exact nature of the relationship between the labels and the examples. Gradually after the supervised training, the algorithm will be able to predict a perfect label for the new data.
Supervised learning has various applications in real-time such as:
- Facial Recoginition
- Targeted Advertising
- E-mail spam filter
Face Recognition is one of the supervised learning applications as our devices have learnt to recognise our face using various algorithms. Our systems are multi-functional nowadays from finding faces and then identifying them is a prime example of supervised learning.
Have you seen how some ads are placed on some websites relevant to what you searched on google or amazon. That is because that too is done by supervised learning. Websites are made to understand what types of advertisements to show to a particular user by using our search history. Therefore all the advertisements’ popularity is dependent on Supervised Learning.
All the emails that we receive in our spam or any other particular category are because of this type of machine learning making sure that the mails platform understands which emails are important and which aren’t, hence categorizing them likewise.
Unsupervised learning is a type of machine learning where no labels are featured, therefore it has to find structures in datasets without any supervision. The system is just provided with some data for the algorithms and some tools are given to analyse the data.
The good thing is that we have a lot of unlabelled data, therefore the algorithms can organise the data in their way and make sense which helps the businesses to make huge profits.
Applications of unsupervised learning in real-time are:
- Customer Issues
- Credit/Debit card issues
- Buying patterns
One of the applications that companies use is that they can group the problems faced by their users/customers and find solutions for them accordingly. Therefore if you have called in for a bug report, unsupervised learning will group your report with other similar reports.
If the spending activity of your card doesn’t match your regular spending habits, then by the help of unsupervised learning you will get a call or an alarm that will confirm if you are the one making payments and no fraud is occurring.
E-commerce is using these algorithms to convey to customers about similar purchasing segments and the recommended section system helping the customers to see the frequently bought together items.
Reinforcement learning is very different from the other two types of machine learning. Using Reinforcement learning, the system can make a sequence of decisions.
The system needs to come with a solution for the problems that are being provided to it as input. This is usually learned with search and trial methods and using this as leverage, Reinforcement learning is the most creative training among the three types of machine learning models.
Some of the applications of reinforcement learning with-real time examples are:
- AI video games
- Data Centres
Reinforcement learning is easily observed in video games, developers are using this type of machine learning on a huge scale. We have seen it grow exponentially in the video game industry with the use of Artificial Intelligence.
A huge cost-cutting is done using reinforcement learning algorithms as it helps us satisfy our requirements. One of the examples is Google’s data centres. Storing data has never been so cheaper, and helps us make the least amount of impact on our environment.
Reinforcement learning is a very complex type of learning and people are still trying to understand how to make the best use of it. It’s being used in robotics, data analytics, aircraft control and business intelligence but still has a long way to go.
With this we come to the end of this article on different types of machine learning. I hope I have answered all your doubts and queries related to the types of machine learning models
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