Which Industries Benefit from Machine Learning?

September 23, 2021
Man pointing to a data visualization

One of the hottest areas in the field of data science is machine learning. Organizations need new methods of filtering and parsing through datasets that are growing larger every day. Machine learning techniques automate the process, but learning algorithms need humans to design and deploy them. If you’re considering a data science career path, a specialization in machine learning is a great way to bring your résumé to the top of the pile when you apply for jobs.


What is machine learning?

Machine learning is an algorithm written to stimulate an artificial neural network to perform a function. An artificial neural network, which is modeled after the biological neural networks in a brain, uses training data to learn to perform the function. You might think of a machine learning system as a statistical modeling algorithm with the sophistication and nuance of a neural network. While artificial neural networks are not nearly as complex or creative as human brains, they have computational capabilities that no human can match.

Unlike algorithms that have fixed and unchanging parameters, machine learning algorithms are able to use information from within the dataset to do predictive analytics and data mining. An unsupervised learning algorithm can develop the capacity to make decisions without explicit programming.

However, machine learning algorithms do not design themselves. The process to develop a machine learning algorithm takes a data science professional who is knowledgeable about machine learning techniques and neural networks, as well as data mining and natural language processing.

The machine learning algorithm is initially tested using training data. The desired results from the training data are known to the programmer. These results are compared to the results of the machine learning analysis. This gives the machine learning engineer opportunities to adjust the learning algorithm until it produces the desired analytics.

Machine learning is an essential component of artificial intelligence. Data scientists trained in machine learning techniques can write algorithms that do data analytics and predictive analytics. This process harnesses data for statistical analysis. Machine learning analyzes more data than a human could process and digests large amounts of data quickly, allowing organizations to understand and respond to market and societal trends in real time. Predictive analytics will spot patterns that can be used to estimate future response rates and return on investment (ROI) for marketing and other projects. Using machine learning, Artificial Intelligence systems can both reevaluate results as more data becomes available and refine their functionality.

Machine learning technologies provide more sophisticated data analysis that can, for example, target a marketing initiative in a fine-grained way that would not be possible without Artificial Intelligence. Machine learning makes predictive analytics more and more accurate - to the point where a company like Amazon might know what product you will order before you hit “purchase.”


Choosing a machine learning model

There are many different models for machine learning systems. Which system you use will depend on the type of inputs you are working with as you design your learning algorithm, your desired outputs, and the types of analysis you want an artificial neural network to perform.

Machine learning models can be classified as supervised or unsupervised. Supervised machine learning systems start with predetermined input-output sets. Supervised machine learning models include decision tree, regression, and Bayesian inference.

Unsupervised machine learning systems do not have predetermined outputs. Unsupervised learning algorithm models include clustering and reduction. An unsupervised machine learning system has a greater capacity to continue to develop its analysis without further input from a machine learning engineer once it is deployed.

Within these two classifications, there are a number of different models for building machine learning systems. For example, the Bayesian model is a probabilistic model that provides a graphical output. Probabilistic machine learning models are useful for predictive analytics. There are numerous types of Bayesian models, including Naïve Bayes. Bayesian models are popular, because they have a good track record for producing accurate and actionable results. In addition, learning algorithms can be built fairly quickly using a Bayesian model.

When you design a machine learning system, you may want to try several different learning algorithm models. The best model for your project is the one that provides the most accurate data along with the most useful data analysis.


Requirements for creating good machine learning systems

A good machine learning system, at its most basic, is one that creates valuable and reliable data outputs. To get from design to function, however, is a process that takes a trained data scientist. That’s where you come in.

One of the requirements for creating an effective machine learning system is having good data inputs. The quality of the training data and output model you use to develop your learning algorithm will play a big role in determining the accuracy and quality of the analysis your machine learning system produces. It’s the GIGO principle (garbage in, garbage out). The first step to creating a good machine learning system is to make sure you have enough data and that the quality of your data is high enough to produce the results you need.

Aggregating the data you need for your learning algorithm is only the beginning. To create a good machine learning system, you will also need to make sure your data sets are clean and free of errors or missing values.

Another important step is to define success. What outputs are you looking for? What type of data analysis will give you the information you need? What learning techniques best fit the machine learning system you are creating?

Before you start, determine how you will assess the outputs of your learning algorithm. Model the learning techniques that your machine learning system will use. Understand what overfitting and underfitting look like for your machine learning model. Overfitting is when the results are too particular to your training data to be useful. The artificial neural network will not be able to use learning techniques to develop a robust and flexible learning algorithm that can be trusted for other data sets. Underfitting happens when a machine learning algorithm lacks one or more of the elements necessary for accurate data analytics.

An important part of the process of creating a good machine learning system is evaluation and adjustment. You will need to fine-tune your learning techniques until the machine learning algorithm consistently and reliably produces useful results that you can trust.

The process from initial conception and design of a machine learning system to eventual deployment can include frustration, setbacks and dead ends. However, once you have developed a learning algorithm that works for your application, it will bring powerful insights for your organization. That is an incredibly satisfying accomplishment. This is part of the experience of working as a data science professional with a specialty in machine learning.


Machine learning is the foundation upon which many of the innovations of modern life are built. For example, the chatbots that pop up on many websites would not be possible without learning algorithms. The bots use machine learning and data mining techniques to find answers to questions you might ask. This reduces the need for companies to hire customer service personnel. It also allows small companies that cannot afford round-the-clock, personalized customer service to provide an AI-powered approximation. In addition, site visitors can often get their questions answered in real time without waiting for an email response or staying on hold on the phone.

If you have shopped on e-commerce sites, like Amazon, you have probably benefited from machine learning algorithms. The feeds that pop up with other products you might like are created with machine learning. Using data about your shopping preferences and the choices of others with similar preferences, the learning algorithm is able to predict what additional products you might be interested in buying.

Machine learning has played a big role in maintaining email as a viable form of communication. Without learning algorithms that detect spam and filter your emails, your inbox would be overrun by an unworkable volume of junk mail. Over time, algorithms to filter spam have become exponentially more accurate, thanks to machine learning.

Google uses machine learning to refine the search results it presents to you. This personalization can have a downside, though. In its attempt to cater to your every wish, the search engine can create a filter bubble that shows you only information that reinforces the perceptions with which you started your search. This can cut you off from differing viewpoints and starve you of information that might be valuable to you. Google has been working on correcting this problem - yet another task for machine learning.

If you use a virtual assistant such as Alexa or Siri, natural language processing allows the assistant to understand your requests and formulate responses in human language with the proper syntax. Machine learning helps the assistant learn your preferences and predict what you will want.

Amazon uses machine learning and predictive analytics for more than recommending products for you to buy. Its neural networks use machine learning techniques to predict your next purchase so Amazon can have it in a warehouse close to you even before you place your order. The company has also turned this machine learning technology into a product that it sells to other retailers, Amazon Forecast.

Machine learning also helps protect consumers against the growing threat of financial fraud. Financial institutions use machine learning techniques to spot patterns that do not fit your purchase pattern, as learning algorithms scan millions of online purchases and withdrawals. That is why your bank may call you to report a suspicious transaction before you even know anything was wrong.

These examples barely scratch the surface of the applications of machine learning techniques and data mining in use today. Many industries rely on neural networks that run learning algorithms and support natural language processing to support their operations. That’s why data scientists with machine learning expertise are in such high demand.


Top jobs for machine learning specialists

Data scientist job trends indicate that training in machine learning and AI are becoming increasingly important. Natural language processing, data mining, learning techniques, and designing and working with artificial neural networks are all areas that are increasingly important as more organizations turn to AI to increase operational efficiency, improve profitability, and provide better customer service.

Data scientists with a specialty in machine learning can command high salaries. For example, Indeed shows an average salary for Machine Learning Engineers of close to $140,000, with a range of up to $230,000. The average salary for Intelligence Specialist positions is over $98,000.

Data from the Bureau of Labor Statistics (BLS) show a median salary for Computer and Information Research Scientists of $118,370 per year in 2018. While the BLS found fewer than 32,000 jobs in this category in 2018, the growth rate for this career path is predicted to be 16% over the next 10 years, a faster expansion than the average rate of job growth.

The BLS data may underrepresent the number of positions for data scientists who can create machine learning systems. A recent search on Glassdoor for Machine Learning Engineer positions produced over 17,000 jobs in the US. In addition to Machine Learning Engineer, job titles included Data Scientist - Analytics; Software Engineer - Machine Learning/AI; Applied Machine Learning Scientist; and Computer Vision Engineer. Glassdoor showed a salary range that topped out at $344,000, showing the possibilities for advancement as a machine learning specialist.

Master of Science in Data Science degree online from the University of Virginia will get you in on the ground floor of the emerging field of machine learning and natural language processing. UVA graduates have gone on to work in government, the medical field, banking, e-commerce, finance and tech, to name just a few.

Machine learning has broad applications across many industries. Here are just a few of the sectors of the economy where machine learning professionals are in great demand.


Machine learning benefits the medical industry

The sector where machine learning may have the biggest impact on society is in the healthcare field. Artificial neural networks can be used to process and analyze medical data to give medical researchers and doctors better insights, quicker than ever before. Learning algorithms can help scientists home in on the most effective treatments, based on data rather than hunches and anecdotal evidence. In the subfield of computer vision-the technical pursuit to allow computers to mimic the human capacity to see and make inferences from image-applications for the health industry include the ability for a computer to scan a medical image and make a diagnosis.

The corporations and nonprofits that run hospitals and health centers value machine learning engineers, because data science can lower healthcare costs while improving patient outcomes. Learning algorithms can allow healthcare systems to provide optimal treatments for their members while reducing the number of ineffective or unnecessary procedures.


Data jobs in government

You may not think of the government as particularly tech-savvy, but government agencies have embraced machine learning techniques. Machine learning can be an effective and economical way for government departments to improve their methods of operation. This can allow agencies to provide better services with limited budgets.

As a machine learning engineer in a government position, you could play a part in designing or refining programs that benefit people at the local, state, or federal level. If you are drawn to public service, a position as a machine learning data scientist for the government could be a rewarding career path.

Data science has offered other government opportunities. The federal government’s Presidential Innovation Fellows is an initiative that connects data and technology professionals with government agencies to solve problems. For other avenues, check out the portal career site USA Jobs has created specifically for data science job openings in government.


Using data mining for retail

E-commerce giants like Amazon are using machine learning and AI to improve customer experience data and increase sales. Data mining allows machine learning engineers to create learning algorithms that provide insights into customer behavior. Natural language processing can provide faster and more responsive customer service. In addition, insights from AI and machine learning algorithms can help retailers streamline all aspects of their supply chain.


Data adds flavor to food service

McCormick and Company partnered with IBM to develop an AI system that will change food science. Famous for their ubiquitous red-capped spices in grocery stores, McCormick’s product developers use the AI-enabled product platform called “ONE” to “learn and predict new flavor combinations from hundreds of millions of data points across the areas of sensory science, consumer preference and flavor palettes.”


AI improves financial service

Fraud detection is just one of the many ways that machine learning techniques benefit the financial services industry. Now that almost all transactions are online, artificial neural networks can use learning algorithms to help investment managers make better decisions.

Developing and deploying machine learning systems requires an upfront investment. The financial services industry understands the return on investment that learning techniques offer. As a result, an increasing percentage of data science positions in the financial sector now call for skills in machine learning.


Why UVA’s online Master of Science in Data Science to become a machine learning data engineer?

The qualifications to become a machine learning engineer include both education and experience. For entry-level or mid-level machine learning positions, experience can include computer engineering work experience, internships, or a background in research into machine learning techniques.

While some machine learning professionals ascend to the field with a bachelor’s degree or less, an advanced degree or master’s degree will put you in a better position to start or advance your data science career with a specialization in machine learning. The online Master of Science in Data Science from UVA’s School of Data Science will give you the well-rounded data science education you need to excel in your machine learning career. You will gain the technical competence you need to develop learning algorithms and deploy neural networks. You will also gain a deeper understanding of learning technologies by delving into the philosophy behind machine learning. You will gain an understanding of the issues around data mining and natural language processing that will allow you to bring a holistic perspective to machine learning applications.

The online MSDS enrolls students from around the country, and the online learning environment allows you to pursue your studies at times that are most convenient to you. An MSDS degree from UVA is an investment in your future that does not require you to leave your current position. You can continue to work while you get your degree online. If you need help with tuition, financial aid is available.

The University’s Master of Science in Data Science online degree program is a two-year course of study. Your coursework will cover the basics of programming and then delve into data mining, machine learning, data analytics and data visualization, as well as ethics for data science professionals. The UVA program has a particular emphasis on machine learning. The last two terms include machine learning courses that will immerse you in Bayesian methods, learning algorithms, machine learning technologies, and unsupervised machine learning.

If you are excited about a career in data science with an emphasis on machine learning, the UVA MSDS program is a great choice. Gain the confidence and knowledge to get the job of your dreams and a career at the leading edge of the data revolution.

Now more than ever, data is shaping the future. Are you ready to contribute your curiosity, imagination, and energy to the world of data science?