ITJobCafe 31 Aug 2018 Viewed 1076 Times Viewed by 531 people

There is no one-size-all when it comes to machine learning. Here's the ultimate guide to machine learning models: what they are, and how they get formed.

Article Image

Machine learning could add up to 250,000 jobs in the US economy in the next few years. This makes it one of the fastest growing industries.

You're probably wondering why machine learning is so important. It's powering the new generation of artificial intelligence. Machines with this ability can learn how to react in their environment.

How do machines learn? ML experts use a variety of machine learning models. If you want to land a job in this exciting area, it helps to know the main models in use today.

All Machine Learning Models Use the Same Fundamental Principle

All machine learning models operate on the same principle. If you take enough data, the machine can analyze it and predict patterns. The more data you give it, the more accurately the machine recognizes patterns.

This is used to inform everything from Netflix recommendations to chatbot responses. It even helps self-driving cars make decisions.

The link between big data and machine learning is clear. Without big data, you can't have machine learning. The machine learning process relies on huge amounts of data for analysis.

There Are Four Main Models

When it comes to machine learning models, there are four in use:

  • Supervised learning
  • Unsupervised learning
  • Semisupervised learning
  • Reinforcement learning

Each of these models has slightly different uses. They also use different methods to achieve their results.

What Are the Differences?

In supervised learning, the machine uses labeled data. It is told the correct output. It then compares its own output and corrects it.

In unsupervised learning, the data isn't labeled. The machine must figure out the correct answer without being told.

Semisupervised learning is a more cost-effective version of supervised learning. It uses a small amount of labeled data to show the machine the right answer. It then allows the machine to work on unlabeled data, deciding for itself the right answer.

Reinforcement learning allows the machine the most freedom. It uses trial and error to discover the actions that yield the greatest rewards.

What Are Different Models Used for?

You might have guessed different learning models achieve different goals. Unsupervised learning, for example, is better at analyzing transactional data. Reinforcement tests video games and robotics.

Machine learning uses many different algorithms. They include decision trees and linear and logistic regression. These algorithms have the machine analyze the input data in different ways.

Machine learning and big data go hand in hand. Without a large dataset for the machine to analyze, it's difficult to get the machine to "find" the right answers.

It's Time to Upgrade Your Machine Learning Knowledge

The benefits of machine learning include better chatbots, self-driving vehicles, and smarter robots. The possibilities are endless.

The different models of machine learning support different outcomes. It's important to learn them all if you want to work in this exciting new field. Take the next step toward a big career move by learning more about machine learning today.