How to Set Up Machine Learning Programs

You've heard of artificial intelligence, but what about machine learning? This offshoot of AI is revolutionizing smart computing, and it's more simple than you might think.

What is machine learning?

The concept of machine learning really is as straightforward as it sounds: the machine is learning. It does this on its own, through the use of algorithms. Different types of algorithms can be used to set up these programs to achieve various results. However, the goal of all machine learning programs is to find patterns in data and make predictions without human manipulation.

So what is machine learning used for? Quite frankly, it can be used for almost anything in just about any field. It's utilized extensively in the medical field to enhance diagnostic tools and specific treatment plans.

Enterprise businesses can also use ML programs to manage risks and support personalized customer service and marketing. Services like Netflix and Amazon use it to suggest new shows and items you may be interested in. ML also powers virtual assistants like Siri and Alexa. Data scientists even theorize that machine learning can be used in the future to create artificially enhanced humans. Imagine being able to download a skill or ability to become a super version of yourself. While this type of utilization is still far off, it's not altogether impossible. In fact, the possibilities of machine learning are virtually endless.

How do you set up ML programs?

All of this sounds really cool, but how does it technically work? The foundation of any machine learning program is rooted in its algorithms. The two main kinds are supervised and unsupervised algorithms, but there are a few other ones too. Let's see how each is used to figure out which one is best for your ML program.

Supervised Algorithms

Most machine learning programs use supervised algorithms to define their structure and purpose. During the training process, you'll feed the computer labeled data paired with desired outputs. If you were trying to teach the computer to learn the difference between cats and dogs, for example, you would feed it images of cats labeled "cat" and do the same for images of dogs. The program then analyzes all of your labeled inputs, finds the correct patterns, and learns how to do the same thing for future unlabeled data. This can be used for regression modeling, classification, decision trees, and more.

Unsupervised Algorithms

The second most common training process is through the use of unsupervised algorithms. As the name indicates, little human interaction is required. You feed the program large volumes of unlabeled data, and the computer will explore it and find any identifiable patterns. Unlike supervised learning, there is no ”correct“ output to be found, only hidden patterns and structures. This kind of ML program is excellent for clustering and anomaly detection.

Semi-Supervised Algorithms

Semi-supervised algorithms combine the two previous methods by inputting some labeled data along with larger amounts of unlabeled data. The machine uses the labeled data to learn more about the unlabeled data, which means the program is constantly becoming more accurate. This training type combines the best of supervised and unsupervised algorithms. It's often used in deep learning, a subset of machine learning that uses algorithms to create artificial neural networks similar to the human brain.

Reinforcement Algorithms

Notably different than the other kinds of ML training, reinforcement algorithms teach the computer through trial and error. The software agents interact with the environment to uncover rewards or errors and use those experiences to make future decisions. The goal of this training is to uncover an action model that produces the best results.

Depending on your goal, you can use any of these algorithms to set up your machine learning program. The great thing about ML is that once you complete the initial training process, the computer does the rest by itself. With new tools like AutoML software, you won't even have to do that part. Users upload their data and select the desired prediction type, and the software chooses the best algorithm for you. Pretty soon, computers will be doing everything for us — and perhaps they'll even be part of us.