Machine learning, artificial intelligence, data science, and deep learning are often used interchangeably. However, it is vital that you understand the nuances you need to know about each term. They are different, and understanding why can help you navigate this new world.
Each piece of the puzzle has its own role to play, which is why you need to understand how ML models, machine learning, and deep learning can also come together to give you tremendous benefits. If you understand where each one belongs, it will help you get other things done equally well. In general, artificial intelligence is the main field. Under artificial intelligence, you have machine learning. Under that, you have deep learning. Each item is a subset of another, and you build on top of each depending on what you are trying to achieve.
Deep Learning vs. Machine Learning
Both machine learning and deep learning have to do with the practice of using data to teach a mathematical algorithm how to make accurate decisions. However, before you can teach that mathematical algorithm how to make the decisions, you still need to teach it what a good result looks like. If it doesn’t know what the outcome should be, it is impossible for you to teach anything. This is where the main differences between machine learning and deep learning come from.
In machine learning, you manually go through and choose the proper features that the algorithm will use to build its knowledge base. Once it has a good understanding of why those features are there, you can then run data through it to teach it how to make good decisions. With deep learning, the process is more automated. It is up to the mathematical algorithm to do all of those things and build the proper ML models. Data science is a factor here, as you need it to build out the means of processing and understanding your structured and unstructured data.
Artificial Intelligence and Everything
Artificial intelligence is a bit more difficult to define compared to other fields. It is the most general field, but there are still certain things that many people can tell if it’s artificial intelligence or not. Artificial intelligence is typically a robotic agent making decisions that seem intelligent to us humans.
If you have a way of using a machine to make good decisions as a human would, many people would say that is artificial intelligence. However, it is quite difficult to understand, and it is also a major problem when working with certain other things. That is one of the many reasons why it is crucial for you to gain a handle on things.
The Importance of Humans In the Process
Ultimately, the most important factor in all of these terms is the human element. You still need a skilled human to extract features and teach the mathematical algorithm what a good outcome looks like. If you don’t, your machine learning results will be awful. You cannot go into a field where you don’t know anything and be successful. That is why this has to be a major part of the process.