Simply speaking, Machine Learning is a field where you teach a program or algorithm to a computer so that it can improve upon a given task progressively. In terms of the research side, machine learning requires mathematical and theoretical modelling of how the process works. With Machine Learning, you will know to build applications exhibiting this iterative improvement.
The world is currently saturated by over-zealous talks of machine learning and artificial intelligence.
But, before you get into this field because of word of mouth, you must know the types of machine learning you might encounter in your learning journey. In this article, we will be discussing the different types of machine learning so that you can create the right learning environment for a given task.
3 Different Types of Machine Learning
#1 – Supervised learning
The most popular machine learning paradigm is supervised learning. Not only is it easy to understand, but also has a simple implementation. You can consider this as using flashcards for teaching a child. Machine Learning professionals describe supervised learning as task-oriented.
It focuses on one task and feeds examples to the algorithm until the task can be accurately performed. In your machine learning career, you will encounter several situations where you will have to use this.
Here are a few examples of common applications that employ this form of learning:
- Advertisement Popularity – Supervised learning is often used for selecting advertisements that can perform well. The ads that you see on the internet while browsing are placed there strategically. The learning algorithm chose these ads because of their popularity and clickability. Even the placement of the ad on a certain query or website is because the learning algorithm found that matching the placement and the ad will be effective.
- Spam Classification – Spam filter is a popular feature in the modern email system that is based on a supervised learning system. By feeding email labels and examples, the systems learn to filter the malicious email out preemptively and ensure that the user is not harassed. It is also possible for the user to provide new labels so that the algorithm can learn the user preference.
- Face Recognition – If you use Facebook, it is highly likely that your face has been involved in training a supervised learning algorithm so that it could recognize your face. Finding faces in a photo and guessing the person in the photo is a supervised learning process. There might be multiple layers involved in it, but it is supervised.
#2 – Unsupervised Learning
This is the exact opposite of supervised learning. There are no labels. Instead, a lot of data will be given to the algorithm along with the tools it needs for understanding the data properties.
From there, it learns grouping, clustering, and organizing data in a way that another intelligent algorithm or a human can make sense of.
This form of learning is extremely important and interesting since the majority of data we have is unlabeled. Having an intelligent algorithm capable of taking terabytes of unlabeled data and making sense of it can be a source of profit for several industries. Here are the different areas where unsupervised learning has found an application:
- Recommender Systems – Whenever you use YouTube or Netflix, you must have noticed their video recommendation system that is often placed in the unsupervised domain. The algorithm knows the video, its genre, its length as well as the watch history of the users. The recommendation system creates a relationship between the data of users who have watched similar videos and other videos that you haven’t watched yet. As per this data, it makes you a suggestion.
- Buying Habits – Your buying habits are stored in a database and that data is often sold and bought actively. Algorithm learning algorithms use these buying habits data for grouping customers into similar segments. Through these, companies can market their products to these grouped segments. It can also be used to resemble a recommendation system.
- Grouping User Logs – Unsupervised learning can be used for grouping user logs and issues. This way, companies can identify central issue themes faced by their customers frequently and rectify them. It can also be used to design an FAQ section for handling common issues or even improving the product. It is highly likely that whenever you submit a bug report or a product issue, it is fed to an unsupervised algorithm that clusters it with similar issues.
#3 – Reinforcement Learning
Reinforcement learning algorithms are behaviour-driven. It has taken influences from psychology and neuroscience.
For a reinforcement learning problem, you need an agent, an environment, and a way of connecting the two using a feedback loop.
For connecting the agent to the environment, you need to give a set of actions that can affect the latter. For connecting the environment to the agent, you need to issue two signals to the agent continually – a reward and an updated stand. Here are a few examples of real-world applications of reinforcement learning:
- Industrial Simulation – Many robotic applications like assembly lines use reinforcement learning to help their machines learn how to complete the tasks without having the processes hard-coded. It is a safer and cheaper option and at times, less prone to failure. Moreover, all of this can be started within a simulation so that you don’t waste any money if the machine breaks down.
- Resource Management – Reinforcement learning can be used to navigate complex environments as it can handle the need of balancing certain requirements. For example, Google’s data centers use reinforcement learning for balancing the need to satisfy the user’s power requirements. It does it efficiently and cuts major costs.
Now, you have an understanding of all three types of machine learning. However, you should note that there will be times when the lines between all the machine learning types will become blurred.
If you want to know more about Machine Learning and get your career started, you can enroll yourself in an AI and Machine Learning Bootcamp that can help you refine your understanding of the field.