What Is Machine Learning? (With Examples, Types, and FAQs)

By Indeed Editorial Team

Published June 4, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Machine learning is a form of artificial intelligence that can learn from data and make decisions with little human involvement. For example, online chatbots, self-driving cars, and devices that recognize your speech use machine learning. Understanding how this form of artificial intelligence works can help you evaluate if you want to follow a career in this field. In this article, we answer the question "What is machine learning?", define artificial intelligence and algorithms, review examples of the current and future uses of machine learning, and answer some FAQs about this technology.

What is machine learning?

Knowing the answer to "What is machine learning?" helps you understand this form of artificial intelligence where systems learn from data, identify patterns, and make changes with little human involvement. Machine learning is a branch of artificial intelligence and computer science that enables computers to use algorithms and data to learn from the interaction with human beings. Artificial intelligence can transform industries and careers as organizations seek to automate tasks, reduce errors, increase customer satisfaction, and save resources. This form of artificial intelligence powers many innovations people use today.

These include recommendations for movies and television shows on streaming services based on your watching habits, music that plays by devices that learn commands from your voice, and responses to online questions you may ask to chatbots about products and services. Machine learning technology involves supervised learning, when computers receive example inputs and outputs, and unsupervised learning when algorithms themselves find patterns in the supplied data. It can also include reinforcement learning when a computer program performs a certain goal, such as playing a game and improves using feedback.

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Artificial intelligence

Artificial intelligence is the science of making intelligent machines, specifically intelligent computer programs, and combines computer science and data to enable problem-solving. Machine learning comprises artificial intelligence algorithms that create systems capable of making predictions or classifications based on data. Most artificial intelligence today focuses on performing specific tasks.

Computer algorithms

Algorithms are a series of instructions to a computer about how to transform facts into useful information. Common examples of algorithms are sorting sets of numbers on spreadsheets and finding routes on maps displayed on computer screens. For example, algorithms enable social media determine how and when posts and advertisements appear on your feed or channel.

Examples of machine learning

Innovations using machine learning continue to grow across most sectors. Without knowing it, you often use this technology dozens of times each day. Some of the most common uses for artificial intelligence are:

  • Computational biology and drug discovery and design: Machine learning can screen large databases. For example, this technology can analyze molecule databases to identify which molecules are likely binding to a particular receptor protein, a function useful for drug discovery and design.

  • Web search and media recommendation engines: You may not realize that machine learning is behind recommendations for TV shows and movies on your favourite media streaming services. The technology recognizes your preferences, finds relevant searches, and returns similar entertainment and advertisements.

  • Game artificial intelligence: Machine learning can play simple games like checkers or complex strategy games like chess and beyond. Thousands of game scenarios allow artificial intelligence to identify moves and strategies as you play.

  • Data modelling: Using large amounts of data creates many opportunities for machine learning to predict results, such as for elections and sports competitions. In business, this predictive modelling identifies market conditions and trends while uses in space and astronomy include operating autonomous robots on Mars and identifying objects in space.

  • Financial and market analysis: A machine learning model can predict if an applicant is credit-worthy, detects credit card fraud, and finds promising trends in the stock market. For example, over time, the technology tracks data to perfect calculations, allowing traders to predict when to sell or buy commodities.

  • Artificial intelligence bots: Organizations use machine learning-backed bots to help translate languages, answer questions, and guide visitors on their websites and social media channels. Bots that may appear as live agents, analyze your responses for keywords and either generate responses directly or pull up links to relevant pages.

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Machine learning in the future

Future uses for machine learning are likely to transform many industries. Advanced artificial intelligence and algorithms that develop learning structures continue creating applications and improving results. Some of the predicted uses of machine learning include:

  • Quantum computing: This advancement has the potential to boost machine learning capabilities by allowing the performance of simultaneous operations and enabling faster data processing.

  • Automated machine learning: Automated machine learning simplifies the process so that most people, with little technical skills, can apply complex machine learning models and techniques without being an expert in the field. For example, this form of automation may give more employees the power to use machine learning to find missing or hidden algorithms quickly.

  • Disease prediction and drug discovery: This technology improves the prediction and prevention of disease by using a wider range of variables, such as research study data, patient demographics, and health records. Machine learning algorithms may reduce drug testing times, predict the impact a drug compound may have on different cell lines and genes, and detect possible side effects.

  • Electronic health records: Electronic health records include huge amounts of data in different forms and from different sources. Machine learning techniques, such as natural language processing and image processing, convert the data into a standard format to streamline and enhance identifying patterns and making disease predictions.

  • Manufacturing: Manufacturers may use ongoing advancements in machine learning to create robots and other autonomous and semi-autonomous machines such as driver assistance and to detect nearby pedestrians. Using this technology may reduce costs, improve supply chain management, and improve quality control.

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FAQs about machine learning

Here are some answers to frequently asked questions about machine learning:

Are machine learning careers in demand?

Machine learning careers are in demand, as employers from most sectors adopt the technology and processes. For example, machine learning allows the supply chain industry to automate materials planning and the finance industry to protect against fraud through automated authentication and fraud pattern detection. In the health care field, those in machine learning careers help build models to diagnose illness and disease and managing resources, while experts in the automotive industry use machine learning on vehicle sensors to collect data to improve predictive maintenance, failure analysis, and autonomous driving.

Related: Everything You Need to Know About Data Science as a Career

What is the connection between the cloud and machine learning?

The cloud can store data plays an important role in the development of data analytics that uses machine learning. The scale of the cloud and its unlimited and inexpensive storage capacity enable machine learning technology to execute thousands of tasks. Cloud providers also use machine learning to power office applications, such as email and voice assistants, and to execute keyboard commands.

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Where do you start with machine learning?

You may take several steps to work with machine learning technology in your current job, or to learn skills for future jobs. First, build your confidence to learn this technical knowledge by breaking it down into many small steps and find a problem that this technology may help you solve. Select a programming language, practice on any available datasets, and build a portfolio to show your skills and document your learning.

What is the difference between machine learning and deep learning?

Machine learning involves algorithms that learn from patterns of data and then apply it to decision-making. Deep learning learns through processing data on its own, like the human brain, where it identifies an idea and analyzes it. Key differences are the way data enters the system as machine learning algorithms require structured data, and deep learning networks rely on layers of artificial neural networks.

How are big data and machine learning related?

Machine learning involves developing systems that learn from experience. Most references to machine learning relate to creating predictive models trained on historical data to make predictions on new data. That historical data may be big data, or very large datasets that are too large to fit on a single computer, such as a single hard drive or in RAM.

How do I apply machine learning to my company or industry?

If you work in data science, you may think of a problem the company faces to see if machine learning algorithms can perform well or better than other methods. Search machine learning literature for applications of machine learning to your workplace to start. You can also search for datasets in the company or from your industry for inspiration and practice.

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