When you begin to learn AI, you come across a term frequently – machine learning. What is it? Types of machine learning, if there are any?
In this article, we’ll be tackling these very same questions.
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Let’s get started.
What is Machine Learning?
Have you ever wondered how does Facebook recommend you friends?
Or how does Amazon recommends your products to buy?
They all use machine learning algorithms.
Machine learning refers to the field of study, which enables machines to keep improving their performance without the need for programming.
Through machine learning, your software and bots can learn new things always and give better results.
Those machines require a lot of programming in the beginning. But once they start the process, they begin to learn different aspects of the task themselves. As machine learning can help so many industries, the future scope of machine learning in bright.
Machine learning is an essential branch of AI, and it finds its uses in multiple sectors, including:
- Healthcare (Read: Machine Learning in Healthcare)
- Social Media
And many more.
How Does Machine Learning Work?
In machine learning, you put in some training data which trains the computer. It uses the data for creating a model, and as it gets new input, it uses them to make predictions.
If the prediction turns out to be wrong, the computer re-starts the process again until it makes a right prediction.
As you must have noticed, the system learns whenever it makes a prediction. It was just a simple example.
Machine learning algorithms are quite complex and require many other steps. Different machine learning tools allow you to explore the depths of Data Science domains, experiment with them, and innovate fully-functional AI/ML solutions. Different tools are designed for different needs. So, the choice of Machine Learning tools will largely depend on the project at hand, the expected outcome, and, sometimes, your level of expertise.
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Different Types of Machine Learning
Here are the following types of machine learning:
Supervised learning is when you provide the machine with a lot of training data to perform a specific task.
For example, to teach a kid the color red, you’d show him a bunch of red things like an apple, a red ball, right?
After showing the kind of a bunch of red things, you’d then show him a red thing and ask him what color it is to find out if the kid has learned it or not.
In supervised learning, you similarly teach the machine.
It is the most accessible type of ML to implement, and it’s also the most common one.
In the training data, you’d feed the machine with a lot of similar examples, and the computer will predict the answer. You would then give feedback to the computer as to whether it made the right prediction or not.
Example of Supervised Learning
You give the machine with the following information:
2,7 = 9
5,6 = 11
9,10 = 19
Now you give the machine the following questions:
9,1 = ?
8,9 = ?
20,4 = ?
Depending on the machine’s answers, you’d give it more training data or give it more complex problems.
Supervised learning is task-specific, and that’s why it’s quite common.
As the name suggests, unsupervised learning is the opposite of supervised learning. In this case, you don’t provide the machine with any training data.
The machine has to reach conclusions without any labeled data. It’s a little challenging to implement than supervised learning.
It is used for clustering data and for finding anomalies.
Following the example we discussed above, suppose you didn’t show the kid different red-colored things in the beginning.
Instead, you put a bunch of red-colored and green-colored things in front of him and asked him to separate them.
Unsupervised learning is similar to this example.
Example of Unsupervised Learning
Suppose you have different news articles, and you want them sorted into different categories. You’d give the articles to the machine, and it will detect commonalities between them.
It will then divide the articles into different categories according to the data it finds.
Now, when you give a new article to the machine, it will categorize it automatically.
Just like other machine learning types, it is also quite popular as it is data-driven.
Reinforcement learning is quite different from other types of machine learning (supervised and unsupervised).
The relation between data and machine is quite different from other machine learning types as well.
In reinforcement learning, the machine learns by its mistakes. You give the machine a specific environment in which it can perform a given set of actions. Now, it will learn by trial and error.
In the example we discussed above, suppose you show the kid an apple and a banana then ask him which one is red.
If the child answers correctly, you give him candy (or chocolate), and if the kid gives a wrong answer, you don’t give him the same.
In reinforcement learning, the machine learns similarly.
Example of Reinforcement Learning
You give the machine a maze to solve. The machine will attempt to decipher the maze and make mistakes. Whenever it fails in solving the maze, it will try again. And with each error, the machine will learn what to avoid.
By repeating this activity, the machine will keep learning more information about the maze. By using that information, it will solve the maze in some time as well.
Although reinforcement learning is quite challenging to implement, it finds applications in many industries.
Applications of Different Types of Machine Learning
Now you know that there are three machine learning types, but where are they used? Well, the following points clarify the same:
- Face Recognition – Recognizing faces in images (Facebook and Google Photos)
- Spam Filter – Identify spam emails by checking their content
- Recommendation systems – Recommend products to buyers (such as Amazon)
- Data categorization – Categorize data for better organization
- Customer segmentation – Classify customers into different categories according to different qualities
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- Manufacturing Industry – Streamline the automated manufacturing process
- Robotics – Teach machines on how to avoid mistakes
- Video Games – Better AI for video game characters and NPCs
Want to Use Machine Learning?
Machine learning is one of the most influential technologies in the world. That’s a big reason why it is so popular nowadays.
Many industries employ machine learning for different purposes so the demand increases day by day. If you would like to know more about careers in Machine Learning and Artificial Intelligence, check out IIIT-B and upGrad’s PG Diploma in Machine Learning and AI Program.
What are the applications of supervised learning?
When we wish to map input labels to output labels, or when we want to map an input to a continuous output, supervised learning is often used. In simple words, when a task involves classification, supervised learning is used. Supervised learning algorithms have several applications, such as detecting faces in images or videos, categorizing text into different classes, and recognizing signatures, etc. Because supervised learning is used to forecast the value of input data, problems like house price prediction, crop sale prediction, weather forecasting, and stock price prediction are some of its other applications.
How is supervised learning different from unsupervised learning?
Supervised learning is a machine learning technique that involves training models with labeled data. To train the model, supervised learning requires supervision, similar to how a student learns in the presence of a teacher. Unsupervised learning, on the other hand, is a machine learning method that uses unlabeled input data to infer patterns. Unsupervised learning aims to extract structure and patterns from unstructured data. There is no need for monitoring in unsupervised learning. The purpose of supervised learning is to train the model to predict the outcome when new data is provided. Unsupervised learning aims to uncover hidden patterns and meaningful insights from an unknown dataset.
What are the advantages of reinforcement learning?
Reinforcement learning can be used to handle extremely complicated problems that are impossible to solve using traditional methods. This approach is preferred for achieving difficult-to-achieve long-term results. This learning paradigm is remarkably comparable to human learning. As a result, it is on the verge of achieving perfection. The model has the ability to remediate mistakes made during the training phase. Once a model has fixed an error, the likelihood of the same error happening is quite low. It can design the ideal model to solve a specific problem. It strikes a reasonable balance between exploration and exploitation.