Top Machine Learning Applications by Industry: 6 Machine Learning Examples

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You likely benefit from machine learning multiple times a day — even if you’re not familiar with the specifics involved. For instance, you rely on machine learning when you use a maps app on your smartphone to get to a friend’s house, or when you ask Siri to play your favorite song one more time. Simply put, machine learning is a field of artificial intelligence that uses data to develop, train, and refine algorithms so they can make predictions or decisions with minimal human intervention.

Machine learning is a rapidly growing field within the technology industry, as well as a point of focus in companies across industries. Given the high demand for machine learning skills in the current job market, understanding the fundamentals can provide a promising pathway for anyone considering a job in tech, or even those looking to change careers.

How Does Machine Learning Work?

The goal of machine learning is to make computers (i.e. machines) learn from experience. This happens through the use of algorithms, which use computational methods to “learn” information directly from data sets. As the available data grows, the algorithms improve their accuracy and performance.

Because machine learning is used across a variety of industries, your areas of interest will dictate how you choose to learn it: Columbia Engineering FinTech Boot Camp, for example, teaches machine learning in finance. Interested in data science? Machine learning is a crucial data analytics skill needed to qualify for in-demand roles. In this article, we will explore how machine learning works in six industries: finance, business, genetics and genomics, healthcare, retail, and education.

Types of Machine Learning

There are three primary techniques used in machine learning: supervised learning, unsupervised learning, and reinforcement learning. Out of the three, supervised learning is the most popular — it trains a model to predict future outputs based on existing input and output data, similar to using flash cards as a teaching method. First, pairs of inputs and outputs are introduced to an algorithm. Over time, the algorithm learns the nature of the input-output relationship to predict an output from a new input. One real-life example of this is your email spam filter — it learns what spam looks like (input), and then learns to separate spam from other mail (output).

Unsupervised learning is quite different. Rather than pre-selecting a preferred output for the algorithm, the algorithm is fed a data set and given the tools to understand its properties. From there, it finds hidden patterns in input data and can then learn to organize the data in logical ways that can help better analyze the set. An example of this is the “recommended” section you might see on a streaming service: the website assesses its videos for length, topic, and other categories, and then uses data on what you’ve watched in the past to make a recommendation for what you should watch in the future.

Reinforcement learning trains a machine through reinforcement mechanisms — similar to the classical conditioning employed by Ivan Pavlov to induce drooling in dogs at the sound of a bell. A reinforcement learning system in its early stages will make many mistakes. However, over time, the machine receives signals denoting error or accuracy and learns from its mistakes to be more successful.

What Is Machine Learning Used For?

Machine learning is behind a wide variety of tools that many of us use every day. It’s present in our social media channels, customer service interactions, and data analytics — and the use cases for machine learning continue to increase. Below are some of the most common uses for machine learning.

Image recognition

Image recognition is one of the most common uses of machine learning. You’ve probably seen it if you’ve ever posted a photo to Facebook and the app suggested you tag a friend — if the machine learning is working correctly, that suggested friend will be the one in the photo. Image recognition is an example of a computer vision algorithm, which breaks an image down into different aspects that are used as reference points. The features of the images are then matched with features of available samples in order to produce a suggestion (e.g., suggest whom to tag in a photo).

Text generation and analysis

Another common type of machine learning algorithm, natural language processing uses deep analysis of text and extracts insights to create an output. They are used to create chatbots as well as an array of text-based services, such as text correction apps like Grammarly, which flags typos and anomalies based on basic grammar rules.

Speech recognition

If you’ve ever used a virtual assistant like Siri or Alexa, you’ve benefited from speech recognition machine learning. These apps use natural language processing to analyze an input (e.g., your voice asking about the weather) and perform the given query to provide a suitable output. These services improve over time, as they are able to learn from user inputs and feedback. Another example of speech recognition is speech-to-text automation, often used for captioning video or audio or sending off a quick text message.

Data analytics

Data analytics is the process of gathering data from data sets, analyzing it to extract relevant insights, and visualizing it logically and holistically. The ability to apply machine learning is an important part of the data scientist role. In fact, some data analysts, like machine learning engineers, even specialize in the field of machine learning. However, data scientists are often required to have general machine learning skills so they can build and train models that can help them make reliable future predictions.

Algorithmic recommendations

As we’ve already mentioned, machine learning is commonly used in any service that recommends content to users (e.g., social media feeds, video platforms, news platforms). These services analyze the content you’ve already consumed — what sorts of videos you like, what types of news stories you like to read — and recommend more of the same without requiring you to manually search for them.

Benefits of Machine Learning

Automation: Machine learning allows companies to automate a wide variety of tasks, making them more efficient and cost-effective.
Less reliance on human interaction: Since machine learning relies almost exclusively on algorithms to get its work done, managers don’t have to worry about balancing team dynamics in order to complete an important task.
Scope of improvement: Because machine learning is always improving and evolving with time, machine learning algorithms are able to constantly build on their own bases of knowledge and functionality.
Efficient data handling: At this point, machine learning is able to analyze any type of data — even the most multi-dimensional — which makes it incredibly useful for data analysis and data science.
Wide range of applications: As you’ll see throughout this article, machine learning is used in nearly every industry today, from healthcare to e-commerce.

graphic highlighting the benefits provided by machine learning including: automation, less reliance on human interaction, scope of improvement, efficient data handling, wide range of applications

How to Learn Machine Learning

If you’re interested in learning more about machine learning or pursuing it as a career, you should educate yourself on the educational resources available to choose from. Since machine learning is such a complex field, it requires specialized education in order to get a job. One great option for getting hands-on experience in a short time frame is a data analytics boot camp: boot camps are faster and more focused than a graduate or undergraduate degree, and they typically offer professional support to qualified learners.

A data analytics boot camp can teach you the necessary skills to become proficient in machine learning — and even connect you with mentors who will help you in your job search. If you’re looking for a more topical focus, you can specifically learn ML for finance in a fintech boot camp. Not exactly sure what fintech is? Generally speaking, it is the integration of technology into financial products and services (e.g., banking applications, investing websites), and as with more general data analytics, it’s a fast-growing career path with plenty of promise for those willing to master the skills required.

Other options include more traditional educational paths like master’s degrees in computer science or data science. These pathways provide a thorough and rigorous education, offering the chance to learn on- or off-campus while potentially exploring the broader fields encompassing ML.

Prepare for your career in machine learning with Columbia Engineering Data Analytics Boot Camp.

When to Use Machine Learning

Machine learning is typically used to predict an output or reveal and understand trends, and is particularly useful when data is structured, or already labeled. Because of the ease with which machine learning is able to categorize and assess this type of data, it’s especially useful for analyzing and organizing data like videos, images, and audio files (e.g., learning whether a photo has a face in it). Machine learning is most helpful when simple rules or computations can’t be used to predict a target value, or when looking at a particularly large data set.

6 Machine Learning Use Cases

Machine learning is used in a wide variety of industries — not just in the tech-heavy companies you might imagine when you think about someone manipulating an enormous data set. Here, we will discuss machine learning use cases by industry, providing some of the different ways that this tool is being used today by a variety of people and companies.

Machine Learning Applications in Finance

In the financial services sector, analysts use machine learning to automate trading activities, detect fraud, and provide financial advising services to their clients. Algorithmic trading requires traders to build mathematical models that can monitor news feeds and trading trends to predict a rise or fall in security prices. Finance companies also use machine learning to detect fraudulent activity by comparing transactions against other existing data points (e.g., they know if that $500 Amazon purchase was something you’re likely to do, or whether it’s completely out of character for you and therefore a little suspicious). In portfolio management, robo-advisors built via machine learning provide investors with automated financial advice based on their goals, risk aversion, and other factors. Across fintech careers, machine learning is an invaluable area of expertise. If you’re interested in learning more about this field, you should consider enrolling in a fintech boot camp.

Machine Learning Applications in Business

Machine learning offers businesses an extensive number of ways to boost their effectiveness, efficiency, and offerings. Chatbots, for example, allow businesses to provide faster, more flexible customer service without employing a call center or making customers wait on hold for the next available representative. Internally, businesses also use machine learning to help with decision support, allowing teams to rely on algorithms to make decisions on resource management or identify trends and problems more quickly. Machine learning also helps businesses deal with customer churn by using data to understand how and why businesses tend to lose customers.

Machine Learning Applications in Genetics and Genomics

The human genome is one of the largest data sets ever studied. Humans contain over 20,000 different genes, each of which has potential for variation. Machine learning allows researchers to better understand different genetic traits and abnormalities as they analyze and understand vast data sets. For example, machine learning helps scientists identify the genetic variants shared in individuals with traits that those scientists are studying, like hemophilia or diabetes, allowing them to better understand where in the genome these disorders originate. Occasionally, it can help researchers understand why they occur in the first place.

Machine Learning Applications in Healthcare

When applied to healthcare, machine learning can help hospitals and their staff make administrative processes more efficient and streamlined, personalize medical treatments, and better understand and track infectious diseases. Technology like PathAI, for example, uses machine learning to help pathologists make more accurate and faster diagnoses, as well as connect patients with new treatments or therapies that might benefit them.

Machine Learning Applications in Retail

As we’ve already mentioned, machine learning can be incredibly helpful in understanding and decreasing customer churn (i.e., the rate at which a business loses customers each year), which is a large point of focus for many retail companies. According to Salesforce, 83 percent of IT experts have found that companies using AI have greater customer engagement. Machine learning also helps retailers synthesize the nearly limitless quantity of consumer data that is available to them but almost impossible to understand by basic human analysis.

Machine Learning Applications in Education

Machine learning can help educational institutions on both process-based tasks and more student-focused initiatives. Statistical models can help understand student progress and needs, while scheduling algorithms can help create more efficient and streamlined schedules for institutions of all sizes.

Machine Learning Examples by Company

Companies of all sorts have used machine learning to expand their offerings and streamline their processes. After completing a data boot camp, you will have the skills to start implementing machine learning in your company — or qualify for a new one. Below is just a small selection of companies that have used these tools to great advantage.

Yelp’s image curation

In 2015, Yelp built a photo classifier that helps classify user-uploaded photos of businesses. For example, if a user went to the local pub, ordered a burger, and uploaded a photo of it to Yelp, the image classifier would be able to properly identify the burger. To build this classifier, Yelp collected the information through photo captions, photo attributes, and crowdsourcing, and then used machine learning to classify future photos. They also allow users to report incorrectly classified photos — a great example of feedback that helps improve ML-built products.

Facebook’s chatbot

In 2020, Facebook introduced a chatbot that was able to converse on a wide array of topics — not just a prescribed set of topics like many customer service chatbots do. According to the MIT Technology Review, this bot, called Blender, was “first trained on 1.5 billion publicly available Reddit conversations.” It was then further developed to focus on conversations containing emotion, information-dense conversations, and conversations between users with distinct and differing personalities.

Amazon’s product recommendations

Once you begin browsing and shopping on Amazon, you’ll start to see suggestions like “Customers Who Bought This Product Also Bought”. These are clear results of machine learning, as they categorize both products and shoppers and use that information to suggest items to users.

Does machine learning require coding?

Yes. If you’re interested in pursuing a career in machine learning, it’s important to know coding basics at the very least. Implementing machine learning does require coding, and therefore machine learning engineers should understand code, even if they’re not always going to be the ones writing it. A coding boot camp is a great place to learn the fundamentals of web development and get hands-on experience using a variety of tools and technologies. Meanwhile, if you’re interested in finance, it’s important to read up on the best programming languages for fintech.

Why is machine learning important?

Data is important to all businesses, from small mom-and-pop shops trying to understand their customers to established global companies like Amazon and Facebook. Data-driven decisions often make the difference between keeping up with competitors and lagging behind goals — machine learning can be key to understanding relevant data and making informed decisions about it.

How can I create a machine learning model?

To create a machine learning model, first define the problem at hand, gather the necessary data, choose your success metrics, and define how you will evaluate your resulting data. Then, categorize the input data, create a benchmark model, and develop improved models. Finally, fine-tune your parameters.

Why do we use machine learning?

Companies use machine learning to analyze the data that is most relevant to their goals and develop tools that can synthesize and utilize that data in order to better meet those goals and serve customers. By creating machine learning models, companies can become smarter about their data and their customers, as the models learn from doing.

What are machine learning algorithms?

A machine learning algorithm is a procedure that turns data into a machine learning model. Algorithms learn from this data, often through pattern recognition. Some examples of machine learning algorithms are linear regression, logistic regression, decision tree algorithms, artificial neural networks, k-nearest neighbors, and k-means.

Apply These Machine Learning Use Cases in Your Company

As we’ve shown, machine learning is an essential tool for any company looking to streamline its processes and better understand its customers and its data. In order to get a strong foothold in the world of machine learning and data science, consider attending Columbia Engineering Data Analytics Boot Camp or, more specifically, learning machine learning for finance in Columbia Engineering FinTech Boot Camp. These tools will not only make your company more successful, but they will also make you a more promising candidate in future job searches.