Machine learning algorithms

An introduction to the math and logic behind machine learning.

What are machine learning algorithms?

Machine learning algorithms are pieces of code that help people explore, analyze, and find meaning in complex data sets. Each algorithm is a finite set of unambiguous step-by-step instructions that a machine can follow to achieve a certain goal. In a machine learning model, the goal is to establish or discover patterns that people can use to make predictions or categorize information. What is machine learning?

Machine learning algorithms use parameters that are based on training data—a subset of data that represents the larger set. As the training data expands to represent the world more realistically, the algorithm calculates more accurate results.

Different algorithms analyze data in different ways. They’re often grouped by the machine learning techniques that they’re used for: supervised learning, unsupervised learning, and reinforcement learning. The most commonly used algorithms use regression and classification to predict target categories, find unusual data points, predict values, and discover similarities.

Machine learning techniques

As you learn more about machine learning algorithms, you’ll find that they typically fall within one of three machine learning techniques:

Supervised learning

In supervised learning, algorithms make predictions based on a set of labeled examples that you provide. This technique is useful when you know what the outcome should look like.

For example, you provide a dataset that includes city populations by year for the past 100 years, and you want to know what the population of a specific city will be four years from now. The outcome uses labels that already exist in the data set: population, city, and year.

Unsupervised learning

In unsupervised learning, the data points aren’t labeled—the algorithm labels them for you by organizing the data or describing its structure. This technique is useful when you don’t know what the outcome should look like.

For example, you provide customer data, and you want to create segments of customers who like similar products. The data that you’re providing isn’t labeled, and the labels in the outcome are generated based on the similarities that were discovered between data points.

Reinforcement learning

Reinforcement learning uses algorithms that learn from outcomes and decide which action to take next. After each action, the algorithm receives feedback that helps it determine whether the choice it made was correct, neutral, or incorrect. It’s a good technique to use for automated systems that have to make a lot of small decisions without human guidance.

For example, you’re designing an autonomous car, and you want to ensure that it’s obeying the law and keeping people safe. As the car gains experience and a history of reinforcement, it learns how to stay in its lane, go the speed limit, and brake for pedestrians.

What you can do with machine learning algorithms

Machine learning algorithms help you answer questions that are too complex to answer through manual analysis. Use cases typically fall into one of these categories.

Predict a target category

Two-class (binary) classification algorithms divide the data into two categories. They’re useful for questions that have only two possible answers that are mutually exclusive, including yes/no questions. For example:

  • Will this tire fail in the next 1,000 miles: yes or no?
  • Which brings in more referrals: a USD 10 credit or a 15% discount?

Multiclass (multinomial) classification algorithms divide the data into three or more categories. They’re useful for questions that have three or more possible answers that are mutually exclusive. For example:

  • In which month do the majority of travelers purchase airline tickets?
  • What emotion is the person in this photo displaying?

Find unusual data points

Anomaly detection algorithms identify data points that fall outside of the defined parameters for what’s “normal.” For example, you would use anomaly detection algorithms to answer questions like:

  • Where are the defective parts in this batch?
  • Which credit card purchases might be fraudulent?

Predict values

Regression algorithms predict the value of a new data point based on historical data. They help you answer questions like:

  • How much will the average two-bedroom home cost in my city next year?
  • How many patients will come through the clinic on Tuesday?

Discover similarities

Clustering algorithms divide the data into multiple groups by determining the level of similarity between data points. Clustering algorithms work well for questions like:

  • Which viewers like the same types of movies?
  • Which printer models fail in the same way?

Start experimenting with Azure Machine Learning

See how different algorithms analyze data by building and deploying your own machine learning models using Azure Machine Learning.