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Machine learning has become something of a buzz term in the technology world over the past few years. It seems like every innovative startup is using machine learning to achieve some goal or another. However, this can leave a lot of people scratching their heads wondering what machine learning actually is. The following primer will help you get up to speed.

Basic Machine Learning Concepts

In the simplest terms, machine learning is a set of techniques and ideas intended to teach computers how to accurately predict the outcome of an input. In a traditional computer algorithm, the programmer completely controls how the system interprets an input. The algorithm may be very complex, but it isn’t capable of changing from what the programmer set up. Conversely, in a machine learning algorithm, the system can “observe” the outcome of an input and “learn” from it. This will help the system to become more accurate over time.

Typically, machine learning software needs to be trained using a lot of inputs. Initially, the creators will provide feedback to the system to help it learn. However, after a while, it became completely self-sustaining. Currently, machine learning is not as advanced as the types of general artificial intelligence that you may see in your favorite sci-fi show. However, it is a big step in that direction.

How AI and Machine Learning Relate

Machine learning is closely related to the more broadly recognized concept of artificial intelligence. ML is a subset of AI.

Artificial intelligence can be defined as a computer system that is capable of doing work that would normally be only possible for a human. This can include tasks such as learning, planning, problem-solving, perceiving, reasoning and more.

In many cases, AI systems can only perform a small subset of the types of the above types of work. A general AI would be capable of all the above and be virtually indistinguishable from a human from a cognitive perspective.

Machine learning is one way to create AI systems. Another option is to design a system that can randomly mutate itself then select the fittest result (creating something akin to natural selection). Some systems are designed by AI experts to mimic human behavior.

Key Models in Machine Learning

There are two major categories of machine learning models: supervised and unsupervised. The former type has a specific target that they are trying to achieve. Models in this category include classification and regression.

Classification involves trying to determine whether an input can be classified as something. For example, whether a picture includes an image of a car.

Regression usually has a numerical goal. It is like solving for the variables in a math problem when you already know the answer.

Unsupervised learning does not have a specific target. Instead, the goal is to understand similarities and patterns.

One style is clustering. This involves grouping inputs based on detected similarities.

Dimension reduction is another form of unsupervised learning. It also involves grouping but has the specific goal of reducing the variables in a dataset.

Applying Machine Learning for Your Business

Machine learning can have a lot of practical applications. It can help you to serve your customers better with intelligent chatbots. It can also help you to detect patterns in your market data to better project possible outcomes of campaigns and products. Alternatively, machine learning could play a role in your product.

Some businesses are built around using machine learning. Others have special products using it such as Facebook and HubSpot. Of course, you don’t have to build your own solution to harness the power of ML. Instead, you can get a third-party product. For example, you can employ a chatbot to handle many of your common customer requests.

Learn More

The more you understand machine learning, the better you can use it to achieve growth for your business. Learn more today and see if there are any interesting ways to harness ML and AI.