Machine Learning: Categories and Definitions
We breathe in a world where technology is growing so fast that many people can’t keep up. One of these technologies is artificial intelligence. This relatively new trend in computer science has brought about fundamental changes in people’s lives. Artificial intelligence is a bit difficult to define, but we can say that it is a combination of different sciences to make machines smart. One of the most popular subfields of artificial intelligence is machine learning, which is hotly debated nowadays.
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Suppose cleaning the floor. If the machine detects dirt, starts cleaning, and terminates the job when the floor gets cleaned, it is much better than when a human does it, as the machine does not get tired, does not get sick, and does not skive off.
The machine must be able to answer the following questions to get ready for cleaning the floor:
- When is the floor dirty?
- When is the floor clean?
- When should it start cleaning the floor?
- How long should it continue cleaning the floor?
This is machine learning that helps the computer to learn and improve its performance continuously.
Since machines do not have brains and nervous systems, machine learning models come to our aid here. The device receives data from the environment, transmits it to learning models. The model makes decisions based on the circumstances.
Artificial Intelligence
Examples of machine learning in everyday life
- Smartphones automatically detects faces and unlocks the phone
- Faces identification of smartphone cameras.
- Instagram, Facebook, and other social networks show ads and suggestions based on the user’s interests and tastes.
- Amazon and other online stores offer exciting products based on user search history.
- self-driving cars (Of course, they are not yet part of everyday life)
These are a few examples of machine learning.
Categories of Machine learning
Machine learning is generally divided into three categories:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
What is supervised learning?
As you can guess from what the name implies, the machine needs an observer or guide in supervised learning, just like someone sitting behind the wheel and learning to drive. In this case, the coach sits next to the person to give him the necessary advice. Similarly, in this type of learning, a series of pre-prepared data is delivered to the machine as a guide, and the machine makes the decisions required according to the relevant model.
What is unsupervised learning?
In this case, the machine does not need help. It can discover the relationships between the data through observations. After receiving various data, the device can find the relationships between them. An example of unsupervised learning is a machine that can distinguish between two vehicle models by clustering based on patterns.
What is reinforcement learning?
In this kind of machine learning, the device continually strengthens itself and tries to learn new things about an agent or environment. This method tries to solve the problem with trial and error. The machine will get a reward if it makes a correct decision and receives a fine if it gets a bad outcome. In this kind of learning, the device tries to be more successful in its future decisions.
The difference between data mining and machine learning
Data mining was introduced in 1930 and aimed to find useful, hidden, and valid information from a massive amount of data. However, machine learning was introduced in 1950 and involves applying a model derived from educational data to new data. Both techniques have something in common: they try to find useful data, but they differ in terms of responsibility, origin, implementation, nature, applications, and techniques used.
Data mining tries to extract meaningful rules and relationships from the data, but the learning machine tries to teach the computer the extracted rules. We can develop our model while applying data mining techniques. The main difference between data mining and machine learning is that information extraction is not possible without human intervention in data mining. Still, in machine learning, human presence is up to the stage of selecting and applying the machine learning algorithm, and then the results are used once and for all. The results of the learning machine are more accurate than data mining.
Is automation the same as machine learning?
If you think machine learning is a new and exciting name for automation, think again. These two branches are entirely different.
Automation is based on the rules—a series of tasks to be done with a predefined pattern. But in machine learning, machines learn new things from their previous experiences. This means that computers can make different decisions or change their performance.
An excellent example of difference between automation and machine learning is email service. When you send emails automatically, you are using automation, and when we put a spam detection filter on it, we engage in machine learning.
tip :
Machine learning strives to improve automation steps significantly.
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