How Does Machine Learning Work?

If you’ve scrolled through recommended friends on Facebook or used Google to search for anything, then you’ve interacted with machine learning. Chatbots, language translation apps, predictive texts, and social media feeds are all examples of machine learning, which is a process where computers have the ability to learn independently from the raw data without human intervention.

But how does machine learning work? Read more to learn about machine learning, the different types of machine learning models, and how to enter a field that uses machine learning.

What is machine learning?

Machine learning is the process of computers using statistics, data sets, and analysis to identify and recognize patterns without the need for a human to be directly involved. The computer uses data mining to gather immense sets of data and analyze it for usable trends and patterns. Consider your streaming service—it utilizes a machine-learning algorithm to identify patterns and determine your preferred viewing material.

Modern computers are the first in decades to have the storage and processing power to learn independently. Machine learning allows a computer to autonomously update its algorithms, meaning it continues to grow more accurate as it interacts with data.

Types of machine learning

Three major types of machine-learning models exist: supervised machine learning, unsupervised machine learning, and reinforcement learning. Each of these types has unique processes and frameworks that make them useful for specific goals. These types of machine learning are:

• Supervised machine learning: This type of machine learning uses very strict parameters for how the algorithm should interact with incoming sets of data. It focuses on how accurate the results are. Businesses tend to employ this type of machine learning for price prediction and trend forecasting.

• Unsupervised machine learning: Within this type of machine learning, labels are not attached to the incoming data. The algorithm analyzes the raw data and then independently creates trends, patterns, and results. It’s used to identify a target audience based on unique factors.

• Reinforcement machine learning: For the algorithm to learn, this type of machine learning uses a punishment/reward system that provides feedback based on trial and error. The successes and failures of the algorithm's results help it become more accurate as it learns. When you’re playing a video game or catching a ride in a self-driving car, reinforcement machine learning is likely contributing to the technology.

Machine learning works best when it’s provided with immense volumes of data. Using millions of examples allows the algorithm to develop a more nuanced version of itself. Finally, deep learning, one of the more recent innovations in machine learning, utilizes vast amounts of raw data because the more data provided to the deep learning model, the better it predicts outcomes. It learns from data on its own, without the need for human-imposed guidelines.

What is machine learning used for?

Professionals use machine learning to understand data sets across many different fields, including health care, science, finances, energy, and more. Machine learning makes analyzing data sets more efficient, which means that the algorithm can determine methods for increasing productivity in various professional fields. To attempt this without the aid of machine learning would be time-consuming for a human.

One example of the use of machine learning includes retail spaces, where it helps improve marketing, operations, customer service, and advertising through customer data analysis. Another example is language learning, where the machine analyzes natural human language and then learns how to understand and respond to it through technology you might use, such as chatbots or digital assistants like Alexa.

Finally, when you’re sitting to relax at the end of the day and are not quite sure what to watch on Netflix, an example of machine learning occurs when the streaming service recommends a show based on what you previously watched.

How does machine learning work?

Machine learning uses statistics to identify trends and extrapolate new results and patterns. It calculates what it believes to be the correct answer and then compares that result to other known examples to see its accuracy. For instance, a machine-learning model might recommend a romantic comedy to you based on your past viewing history. If you watch the movie, the algorithm is correct, and it will continue recommending similar movies. If you reject the movie, the computer will use that negative response to inform future recommendations further.

Machine learning evaluates its successes and failures over time to create a more accurate, insightful model. As this process continues, the machine, with each new success and failure, is able to make even more valuable decisions and predictions. These predictions can be beneficial in fields where humans might not have the time or capability to come to the same conclusions simply because of the volume and scope of data.

Who uses machine learning?

Professionals who require nuanced data analysis often use machine learning. These professionals might include academic researchers working with large data sets, companies looking for improved operations in fields such as health care, manufacturing, or banking, and social media platforms that involve photo tagging and identification.

Pros and cons of using machine learning

Machine learning has both its benefits and drawbacks. One of the biggest pros of machine learning is that it allows computers to analyze massive volumes of data. As a result of this detailed analysis, they can discover new insights that would be inaccessible to human professionals. For industries like health care, the ability of machine learning to find insights and create accurate predictions means that doctors can discover more efficient treatment plans, lower health care costs, and improve patient outcomes.

The cons of machine learning are twofold. First, it’s important to remember that computers are not interacting with data created in a vacuum. This means you should consider the ethics of where the data originates and what inherent biases or discrimination it might contain before any insights are put into action.

Second, because a computer isn’t a person, it’s not accountable or able to explain its reasoning in a way that humans can comprehend. Understanding how a machine is coming to its conclusions rather than trusting the results implicitly is important. For example, in a health care setting, a machine might diagnose a certain disease, but it could be extrapolating from unrelated data, such as the patient’s location.

How to get started in machine learning

If you want to build your career in this field, you will likely need a four-year degree. Some of the degrees that can prepare you for a position in machine learning are computer science, information technology, or software engineering. While pursuing one of these bachelor’s degrees, you can learn many of the foundational skills, such as computer programming and web application, necessary to gain employment within this field.

If you choose to focus on a career in machine learning, an example of a possible job is a machine learning engineer. In this position, you could create the algorithms and data sets that a computer uses to learn. According to Glassdoor’s December 2023 data, once you’re working as a machine learning engineer, you can expect to earn an average annual salary of $125,572 [1]. Additionally, the US Bureau of Labor Statistics expects employment within this sector of the economy to grow 23 percent through 2032, which is a pace much faster than the average for all jobs [2].

Getting started on Coursera

Sharpen your machine-learning skills and learn about the foundational knowledge needed for a machine-learning career with degrees and courses on Coursera. With options like Stanford and DeepLearning.AI’s Machine Learning Specialization, you’ll learn about the world of machine learning and its benefits to your career.