machine learning is mainly divided into three well-known categories: supervised learning, unsupervised learning, and reinforcement learning. In a world saturated with artificial intelligence, machine learning, and extensive research on these topics, it is not bad to fully know and understand the types of machine learning that we may encounter.

But before entering the fascinating world of machine learning, we must take Python training very seriously and have a good plan for it. Because Python and machine learning are tied together. It is one of the most important applications of Python language for machine learning and deep learning.

For most computer users, familiarity with different types of machine learning can help in understanding how to use them in the programs they use. It is essential for professionals who create these programs to understand the different types of machine learning so that they can use the right machine learning method for any specific project they may be facing and better understand how it works. So stay with us to have a closer look at this issue.

Supervised learning

Supervised learning is the most popular method for machine learning. Because it is understood in the simplest form among other methods, and it is also very simple to implement. This type of learning is very similar to teaching a child using flash cards.

Using the input data in the form of labeled examples, we can create a learning algorithm. We let the algorithm predict the label for each example and give it feedback on whether it predicted the correct answer or not.

Over time, the algorithm learns to approximate the exact nature of the relationship between samples and their labels. Once the machine is fully trained, the supervised learning algorithm can predict a good label for a new example it has never encountered before.

Supervised learning is often described as task-oriented learning. And is very focused on a single task. During the supervised learning process, we give the algorithm more and more examples until it can accurately perform the task we want.

Use cases of supervised learning

Find popular ads

Choosing ads that perform well is often a supervised learning task. Many of the ads you see when you browse the Internet have been chosen by machine learning to show you. A learning algorithm has said which ads are reasonably popular and most likely to be clicked.

Classification of spam

If you use email, you must be familiar with the spam filter. This spam filter is a supervised learning system. These systems learn how to proactively filter out malicious emails so they don’t harass the user. Many of these also behave in such a way that the user can provide new tags to the system so that the system can recognize and learn the user’s preference.

Face Recognition

Do you use Facebook? Most likely your face has been used as data in a supervised learning algorithm. And the system used in Facebook is trained to recognize your face. The system detects faces and guesses who is in the photo and identifies users. This practice is a supervised process.

Unsupervised learning

Unsupervised learning is the exact opposite of supervised learning. This type of learning has no labels. Instead, the algorithm is given a lot of data and given the tools to understand the characteristics of the data.

In this mode, the model learns through observations and discovers patterns and structures in the data set. When a data set is introduced to the model, the model automatically discovers the relationships and patterns in them by grouping, clustering, or organizing the data.

Since unsupervised learning is based on data and its characteristics, it can be said that unsupervised learning is data-oriented learning. The results of the unsupervised learning task are controlled by the data and how they are formed.

What makes unsupervised learning interesting is that the vast majority of data in the world is unlabeled. Having intelligent algorithms that can take our terabytes of unlabeled data and find meaning and connections between them is a huge source of potential profit for many industries and alone can help increase productivity in several areas.

Let me give an example. Imagine if we had a huge database of every research paper ever published. And have unsupervised learning algorithms that know how to categorize these so that you’re always up to date with the latest developments in research on a particular topic.

Now, you start creating a research project yourself and plug your work into this network so the algorithm can see it. As you write and type your work, the algorithm will give you suggestions on related tasks that might help you. Suggesting works you might want to cite and works that might even help you advance your research scope. Undoubtedly, such a tool can greatly increase your productivity.

Examples of the effectiveness of unsupervised learning

Suggested systems

If you’ve ever used YouTube or Netflix, you’ve likely come across a video recommendation system. These systems are often in the domain of unsupervised learning. The system gives you new suggestions based on the movies, their length, their genre, the viewing history of many users, users who have watched the same movies as you, or other movies that you haven’t seen yet, and with the help of analyzing this data.

  Customer buying habits

Chances are your shopping habits are in a database somewhere. These shopping habits can be used in unsupervised learning algorithms to group customers into similar shopping segments. This method helps companies to market to these grouped segments and can even act as a recommendation system.

Grouping of user records

We can use unsupervised learning to group user records and problems. This can help companies identify key issues to solve their customers’ problems and fix those problems by improving a product or designing FAQs to address common issues.

In any case, this is done automatically. If you’ve ever had a problem with a product and sent a bug report to a company, you’ve probably given it to an unsupervised learning algorithm to categorize it with other similar issues!

Reinforcement learning

Reinforcement learning is quite different compared to supervised learning. In fact, with reinforcement learning, we can easily see the relationship between supervised and unsupervised learning (presence or absence of labels). Understanding reinforcement learning is a bit difficult. Some people try to simplify reinforcement learning by describing it as a type of learning that is related to time-dependent sequences, however, in my opinion, this type of learning is simply confusing.

I prefer to describe reinforcement learning as learning from mistakes. A reinforcement learning algorithm will initially make many mistakes; As long as we provide some kind of signal to the algorithm that associates good behaviors with a positive signal and bad behaviors with a negative signal. This way we can strengthen our algorithm to favor good behaviors over bad ones. Over time, our learning algorithm learns and makes fewer mistakes than before. Reinforcement learning is very behavior oriented.

However, for a real understanding of reinforcement learning, let’s give a specific example. Let’s learn an agent for the Mario game:

For any reinforcement learning, we need an agent and an environment, as well as a way to connect the two through a feedback loop. To connect the agent to the environment, we give it a set of things it can do that affect the environment. To connect the environment to the agent, we continuously issue two signals to the agent: an updated state and a reward (our reinforcing signal for the behavior).

In a Mario game, our agent is our learning algorithm and our environment is the game (most likely a specific level). Our algorithm has a set of steps ahead. Over time, our updated state will be every frame of the game, and our reward signal will be the change in score. If we connect all these components, we will set up a reinforcement learning scenario for the Mario game.

Application of reinforcement learning in the real world

Video games

One of the most common environments for reinforcement learning is learning through play. Like Google’s reinforcement learning programs, AlphaZero, and AlphaGo. Currently, I don’t know of any game that has a reinforcement learning factor that relies on artificial intelligence, but I can imagine that this interesting idea will be realized soon.

Industrial simulation

Many robotic applications are used in assembly lines. It would be better for our machines to learn to do their jobs without having to decode them in the process. This can be a cheaper and safer option. It may even be less prone to failure. We can also adjust our devices to use less electricity to save money. Also, with the help of this simulation, we prevent the device from being damaged.

Resource management

Reinforcement learning is suitable for navigating complex environments. This type of learning can solve the need for balance in some situations. For example, Google data centers. They use reinforcement learning to meet energy demands and maintain balance, reducing capital costs. How does this issue affect our work and ordinary people? Making data storage cheaper for us as well as having less impact on the environment we all live in.

Integrating types of machine learning

Now that we’ve talked about the three different categories of machine learning, it’s important to note that the line between these three types of learning is blurred. Many tasks can easily be expressed as a type of learning and even transformed into another paradigm.

For example, choose a suggested system. We discussed it as an example of unsupervised learning. This system can also easily be modified to a type of supervised learning. For example, according to users’ viewing history, predict whether a certain movie should be recommended or not.

Also, the more interesting idea is that we can build this kind of learning and design components of systems that learn in a learning way, but integrate into a larger algorithm. For example, for the algorithm that plays Mario, why not give it a supervised learning ability to recognize and tag enemies? Or a system that classifies sentences, why not create the ability for the system to learn through an unsupervised process; Represent the meaning of the sentences?

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