Machine learning has come a long way from science fiction fantasy to a reliable and versatile business tool that augments many elements of business operations.
Its impact on business performance can be so significant that implementing machine learning algorithms is required to remain competitive in many fields and industries.
Implementing machine learning into business operations is a strategic move and requires a lot of resources. Therefore, it is essential to understand what you want ML to do for your business and what benefits different ML algorithms bring you.
What is Machine Learning?
The ability of a machine to learn something without needing to be taught to do that specific activity is known as machine learning. It’s a field where computers use a massive set of data and apply algorithms to “train” and make predictions. Machine learning training means feeding a large amount of data into an algorithm and allowing the machine itself to learn more about the information it processes.
Answering whether an animal in a photo is a cat or a dog, recognizing obstacles in front of a self-driving car, detecting spam, and recognizing the speech of a YouTube video to generate captions are just a few examples of predictive machine learning models.
Why Machine Learning?
Let’s start with an example where a machine beats a strategy game by learning itself. In 2016, the world’s strongest Go player (Go is an abstract strategy board game invented in China over 2,500 years ago), Lee Sedol, faced off against Google’s DeepMind Machine Learning program, AlphaGo. AlphaGo won the 5-day match.
One takeaway from this example is not that a machine can learn to conquer Go, but how these revolutionary advances in machine learningthe ability of devices to mimic the human braincan be applied is beyond imagination.
Around the world, machine learning has made its way into many different business areas. It’s all because of the incredible power of machine learning to drive organizational growth, automate manual and mundane tasks, enrich the customer experience, and meet business goals.
Machine learning algorithms run on a variety of programming languages and techniques. However, these algorithms are trained using different methods, of which the three main types of machine learning are:
Supervised learning
Unsupervised learning
Reinforcement learning
Supervised Learning
Supervised learning is the most widespread machine learning paradigm. It is the simplest to comprehend and put into practice. It is a lot like instructing a toddler using flash cards. It is very similar to teaching a child using flash cards.
Given data in the form of labeled examples, we can feed these example-label pairs to the learning algorithm one at a time, allowing the algorithm to predict the label for each example and giving 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 examples and their labels. Once fully trained, the Supervised Learning algorithm can observe a new, previously unseen example and predict a good label for it.
You are most likely to encounter this type of learning because it occurs in many of the following typical applications:
Spam Classification: If you’re using a modern email system, chances are you’ve come across a spam filter. This spam filter is a supervised learning system. Examples and labels of e-mails (spam/not spam), these systems learn how to preemptively filter out malicious e-mails, so their use is not bothered. Many of them also behave so that the user can provide new labels to the system, and it can learn the user’s preferences.
Face recognition: Do you use Facebook? Your face has most likely been used in a supervised learning algorithm trained to recognize your face. Having a system that takes a photo, finds faces, and guesses who is in the photo (suggests a tag) is a supervised process. It has multiple layers, it finds faces and then identifies them, but it’s still under surveillance.
Unsupervised Learning
Unsupervised learning is pretty much contrary to supervised learning. It has no labels. Instead, our algorithm would be given a lot of data and the tools to understand the properties of the data. From there, it can learn to cluster, group, and/or organize data so that a human (or another intelligent algorithm) can step in and make sense of the newly organized data.
For example, what if we had an extensive database of all the published research papers and Unsupervised Learning algorithms that could group them so that you would always be aware of the current developments in a particular area of research? Now you start a research project and plug your work into this network that the algorithm sees. As you write your paper and take notes, the algorithm gives you suggestions for related jobs, works you might want to cite, and works that might even help you move this area of research forward.
Since unsupervised learning is based on data and its properties, we can say that unsupervised learning is data-driven. The results of an undirected learning task are controlled by the data and how it is formatted. Some areas where you may encounter unsupervised learning are:
Recommendation systems: If you’ve ever used YouTube or Netflix, you’ve most likely come across a video recommendation system. These systems are often located in an unattended domain. We know things about the videos, maybe their length, genre, etc. If we consider users who have watched similar videos to you and then enjoyed other videos that you haven’t seen yet, the recommender system can see this relationship in the data and prompt you for such a suggestion.
Clustering user logs: We can use unsupervised learning to cluster user logs and problems, which is less user-intensive but still highly relevant. This can help companies identify the core themes of problems their customers are experiencing and fix those problems by improving the product or suggesting FAQs that address common issues. Either way, it’s something that’s actively being done. If you’ve ever submitted a product issue or bug report, chances are it’s been passed to an unsupervised learning algorithm that groups it with other similar issues.
Reinforcement Learning
Compared to supervised learning and unsupervised learning, reinforcement learning is very different. The connection to reinforcement learning is not as clear-cut as the relationship between supervised and unsupervised learning (the presence or lack of labels). Some people describe reinforcement learning as a form of learning that depends on a time-dependent sequence of labels in an effort to link the two concepts more closely. But in my opinion, this merely complicates everything.
The same behavior-driven principles apply to reinforcement learning. It is influenced by the study of psychology and neuroscience.
In a Mario game, our agent is our learning algorithm, and our environment is the game (probably a specific level). Our agent has several promotions. These will be our button states. Our updated state will be every game frame over time, and our reward signal will be the score change. As long as we connect all these components, we will create a learning scenario to enhance the Mario game.
A real-world example of reinforcement learning
Video Games: Playing video games is one of the most popular ways to get reinforcement learning. For reinforcement, have a look at AlphaZero from Google’s training software and AlphaGo, which has mastered the game of Go. Maria in our case is likewise typical. I’m unaware of any production-level match with a reinforcement learning agent deployed as the game’s AI. Still, I can imagine this will soon be an exciting option for game developers to take advantage of.
Industrial Simulation: It is advantageous for our robots to learn how to fulfill their duties without having to hard-code their operations in many robotics applications (think assembly lines). This may be a less risky, safer, and even more affordable solution. To save money, we can also encourage our equipment to consume less electricity. Additionally, since we can simulate everything, we won’t lose money if we accidentally damage our system.
Conclusion
As you can see, different types of Machine Learning algorithms solve various problems. The combination of different algorithms enables performance capable of handling a wide range of tasks and extracting valuable insights from all kinds of information.
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