Artificial Intelligence is everywhere. It is being used in the fields like entertainment, healthcare, finance, and even retail. If you study AI a little bit, you will come across a term called Machine Learning (ML). It works according to the Machine Learning algorithm or MLA.
It is known as one of the foundation blocks of Artificial Intelligence. It’s important for people who want to raise their knowledge of AI to understand it. That is what we will try to help with in this article. Here, I am going to describe this term in detail and understand how it actually works.
So, let’s begin!
What is a Machine Learning Algorithm?
A Machine Learning Algorithm is a set of different rules and instructions that an AI uses to process data. This data processing is done to complete different computer tasks, including discovering new data, learning patterns, and predicting an output from a given data set.
In simple words, the role of AI depends on the machine learning algorithms to proceed with a task. The algorithm uses this data to perform the tasks mentioned earlier. Moreover, the more data you feed to the algorithm, the better it performs. The algorithm learns from this data without the use of a complex computation.
The use of this algorithm has increased a lot in the last few years. Its primary importance and quality are that it can process and handle huge data sets faster and more accurately than humans.
Also Read: What Is AI Hallucination? Steps to Avoid
Main Categories of Machine Learning Algorithm
ML Algorithms are categorized in two basic ways:
Supervised Learning
In Supervised Learning, experts provide the algorithms with data that is clearly labeled. It is also completely overviewed by a scientist. These algorithms are also given instructions about both input data and the desired output (output that the scientist wants from it).
This category consists of two parts. The first one is classification. It helps the algorithm divide data into relevant groups. For example, if you give it ten pictures of dogs and cats, it will divide them accordingly (pictures of dogs on one side and cats on the other).
The second part is regression. It uses regression to make possible predictions of an event using a given set of data. This type is used in sections like sales to predict the result of a strategy before implementation.
Unsupervised Learning
Unsupervised Learning works differently from Supervised Learning. In this type, the data that you give to the algorithm is not labeled or classified. So, the algorithm has to process it on its own.
One of its examples is clustering algorithms. They are used to manage unsorted data according to the similarities and differences of the elements present in that data.
These types of algorithms are used to find similarities and connections between different things. For example, if you feed it a data set of various people who buy chocolate from one brand, it will tell you the people from it who buy chocolate from some other similar brand.
How Does a Machine Learning Algorithm Work?
Understanding the workings of a Machine Learning Algorithm is easy. Here are more details about it:
Data Input
The first thing in the working procedure is the input of data. This data can be of different types, including text, images, videos, graphs, etc. The primary purpose of inputting this data is to help the algorithm learn from it.
Data Learning
Once the data scientist has input the data into the algorithm, the algorithm starts learning from it. The learning takes place according to the categories I mentioned earlier (supervised, unsupervised). So, you can say that the type of data impacts how the algorithm will learn and work with it.
Creating a Model
After learning the received data, a Machine Learning Algorithm makes a model. Furthermore, it will help it do different things such as organizing the elements of the data, making predictions, etc. It is actually a set of rules that the algorithm will use to work on any data that you provide it.
Output
After model creation, the algorithm starts giving outputs to the AI system. These outputs depend on the model it has created. For example, it can tell the system whether the given picture is of a cat or not. Some models will give this output in the form of predictions.
Conclusion
Machine Learning Algorithm is a set of instructions used by an AI system/computer to process data. The algorithm processes this data in different ways. It depends on the type of algorithm and the data you input.
The main duty of these algorithms is to give desired outputs that can be of different types. Understanding it working is not a difficult thing to do. I have explained it in simple words in the article above.
Frequently Asked Questions (FAQs)
Is the Machine Learning algorithm used in ChatGPT?
Yes, ChatGPT uses a large Language Model (LLM), which is built on this algorithm.
Can these algorithms make mistakes?
The answer to this question is yes. These algorithms make mistakes that you may find if you have expertise in the topic.
Are MLAs used in self-driving cars?
Yes. Self-driving cars use machine learning algorithms as well to drive cars automatically.
