If you work in finance, sales or within a tech company, you have probably heard about something called machine learning. You may have heard how it can make predictions and solve problems faster. “What is it exactly?”, you ask yourself. This is where we come in.
To help you have a better basic understanding of this new trend, we will explain what machine learning is, how machines learn, and some examples of machine learning. It is important to know that this is just a brief overview of machine learning, as there is a tremendous amount of information on the subject.
What is Machine Learning?
So what is machine learning? It is important to note that machine learning is not artificial intelligence (AI), but is rather a subset of AI. In programming, developers will use algorithms to tell the application what steps it will need to take to solve a problem. Think of this as giving the computer a step-by-step guide as to complete the task(s) at hand.
When things get complicated, a programmer may have a harder time creating algorithms manually. This is where machine learning picks up the brunt of the work. The machines now can learn what to do on their own rather than being told exactly what to do. But how do they learn? Let’s break that down in the next section.
How Do Machines (Computers) Learn?
So we have established that machine learning has the computer learn what to do in order to make decisions and provide predictions. But how do they learn in order to make sound predictions? The first thing a computer needs to learn is data. Lots and lots of data. Without any data to help base decisions on, the computer(s) can not do anything! So how does a computer use the data provided to learn? That all depends on the type of learning used.
There are three kinds of learning methodologies used in machine learning. These are:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Supervised learning involves using sets of data called training sets. The data in these sets When choosing the data for these sets, the programmer will need to find data that represents the real-world use of what the instance of machine learning has been designed for. Once the machine has the training set, it takes these inputs and turns them into something called a feature vector. These vectors contain features that help describe the objects (data) in these training sets. The programmer will need to find the right number of features, as too many or too little will hamper the accuracy of the data provided in the output.
Supervised learning also involves working on mapping each input from the data sets into a specific output, such as if the data fits the description of a disease or not a disease for example.
While supervised learning uses training sets and is supervised by programmers, unsupervised machine learning is the complete opposite (as the name suggests). Instead of giving the machine labeled data for training sets and mapping the data to a specific output, input data is only provided, and this data is not labeled. Here, the algorithms provided to the machine are left on their own to discover the structure of the data provided.
Unsupervised machine learning is useful when you want to find either groupings within the data, such as grouping a business’s customers based on what they have purchased, or association, where you try to find rules that describe portions of the data, such as seeing that a customer who purchased a particular item also tends to buy another particular item.
Reinforcement learning is rather unique compared to supervised and unsupervised learning. Reinforcement learning, as the name suggests, is a goal-based method. This method uses an “agent” to complete actions. If the agent makes a wrong decision, it will be penalized. If it makes a correct decision, it will be rewarded.
Of course, this means that the agents will keep performing actions within the environment it is in. The environment takes the agent’s current state (the state as the exact spot an agent is in, such as a square on a chess board, at a specific time, for example) and determines the reward for the agent if it earned one. This means that reinforcement algorithms may make so many correct decisions, that it can beat Go world champions at their own game!
Machine learning is growing in popularity. With the ability to make predictions with large amounts of data, machine learning can be beneficial to many fields.
Of course, there is a lot more to machine learning than what is in this article. If you want to learn more, we will be looking more in depth at some aspects of machine learning in other articles, so look out for new content!
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