What is Machine Learning? – Definition, Importance, Uses and More

What is Machine Learning? Well, Machine Learning is simply an answer to the age old question, what is the best way to learn? The field of machine learning actually is involved with the very same question as to what is the best way to learn, but instead of humans being the ones who are trying to figure out the answers, it’s actually computers. Computers have been learning for decades, and as time has passed, computers have become more capable.

When people ask what is machine learning, they are typically referring to a system of teaching a computer to recognize patterns or to recognize a particular type of input data. Basically, the goal of a supervised learning system is to give a computer the right type of training in the form of labeled inputs data to basically tell the computer what it needs to do next. It may seem like this is very easy, but in actuality it isn’t. Computers aren’t good at anything unless they are told specifically what to do, and humans aren’t interested in providing that type of information to a computer.

Humans aren’t bad at providing information or even creating new information, but there are plenty of things they are really good at, such as being able to predict the future. Humans can predict things about the future based on current information, but a machine doesn’t have that luxury. What is Machine Learning? If you’re wondering what this means, it simply means if you’re trying to create an artificial intelligence system that will be able to predict the future, or help you in some way as to how to go about making decisions, then you’re going to need to have some knowledge of artificial intelligence and learning systems. If you’re not familiar with these concepts though, it’s safe to assume that you understand the basics of computing and the way it works, but you might be surprised by the difference machine learning makes when it comes to making decisions.

Basically, supervised learning refers to a set of education methods which involve some type of reward or punishment for your actions. For instance, you might be taught to count backwards from one to ten. If you do this correctly, then you’ll receive a reward. Now imagine if instead of count-scoring, you were given the option to buy an item off of a vending machine based off of the choices you make. If you made choices that resulted in a purchase, then you would receive a monetary reward. This is the basic outline of supervised learning, and it’s where most of the work is done with unsupervised machine learning.

Unsupervised learning deals more with reinforcement. Basically, you’re taught a task based on the data you provide (usually from the example above), and you are not told specifically what action to take. Instead, you are taught only what you should do based upon the actions that you’ve made so far. This is often used by personal assistants like sales people, or medical assistants who need to do a lot of data collection themselves.

The final method for artificial intelligence prediction is called data analysis. Data analysis is pretty much the same as supervised learning, except that you’re not restricted by the data sets that you’re working with. Instead, you can use mathematical algorithms to predict which actions will lead to which results, and then use those results to make predictions. Data analysis is probably the most widely used form of machine learning currently, and it’s used for everything from stock market predictions to the weather forecast.