Machine learning came out of the field of artificial intelligence (AI). It is the process of computers having the ability to learn from experience. For instance, software is coded with a generic algorithm that it can build upon.

As technology continues to evolve, machines can now learn from inputs. We need machine learning to filter through and analyze massive sets of complex data. Specific types of machine learning algorithms include:

  • Unsupervised learning (Discovering the rules, and groupings of data, without the corresponding output)
  • Supervised learning (Learning from previous examples)
  • Reinforcement learning (Learning through a series of rewards and punishments)

In addition, examples of machine learning at work include:

  • Facial recognition
  • Predicting the weather
  • Medical patient diagnosis
  • Filtering emails

Only five decades ago, machine learning was considered science fiction. Famous writers, from Jules Verne to George Lucas, had the bright imagination to humanize artificial entities.

In 1952, IBM's Arthur Samuel created a program to play checkers. Samuel played with the program so often that it was able to improve with each consecutive game. It was Samuel who first coined the term "machine learning." [Rosenblatt, Frank. "The perceptron: a probabilistic model for information storage and organization in the brain." Psychological review 65.6 (1958): p. 386.]

Today, machine learning has become embedded technology many of us take for granted in our daily lives. There are many industries that currently use machine learning to help improve their daily processes.

Through machine learning, businesses can move ahead of descriptive and predictive analytics to prescriptive analytics without a hitch. Keep reading to learn more.


Whether marketers work in the B2C or B2B fields, they are now utilizing machine learning to grow their audiences and personalize their messages to make them much more relevant. To start, many marketers are realizing machine learning provides a gold mine in terms of competitor analysis. Through software, using machine learning, marketers can track the conversions made through the experiences their competitors provide.

Some say competitors are the best friends you can have. As a result, it is critical to keep an eye on what your competitors are doing to ensure you will always have a place in your industry. With machine learning, marketers are given an in-depth view of what works for their competitors. Even with mounds of data, machine learning is helping marketers to get through it all.

Another way marketers are currently using machine learning is through social listening. Today's markets are consumer-driven. These customers want to be seen, heard, and understood.

Naturally, there just aren't enough hours in the day to try to understand every single prospect or customer on social media. Instead of spending thousands of hours combing various platforms, marketers are using machine learning instead.

In addition, marketers are using machine learning to decipher complex algorithms when deciding how to optimize their marketing content. Machine learning software can help marketers figure out how to set the tone for their messages and decipher the specific words that will best resonate with their target audience.


Agriculture is another industry moving fast into the future with the help of machine learning algorithms. For instance, machine learning is being used to forecast the weather for chosen locations. It is also being used to figure out if machinery can access remote fields.

Then, there are options to find out whether pests are prominent based on environmental surroundings.


Since the construction industry is vital to the growth of any nation, it won’t be left out of the machine learning revolution. On any given day, a construction project can be embedded with hundreds of change orders and thousands of issues.

But machine learning is now being used to help teams determine the most critical tasks that need attention ASAP. Machine learning is also being used for safety monitoring, finding safety hazards, and identifying issues such as missing materials or equipment.


Manufacturing is process work that drives innovation. During the manufacturing process, companies are always trying to determine the specific factors — such as quality and efficiency — that will drive commercial success. Thanks to machine learning, the hours needed for the process have been cut significantly.

According to a study by McKinsey & Company, machine learning has helped to reduce annual maintenance costs of industrial equipment by 10% through predictive maintenance.

Machine learning has also helped to reduce scrap rates in the semiconductor manufacturing industry. Furthermore, according to PwC, machine learning will contribute to a 31% growth rate for connected factories over the next five years. [p. 48, PDF]

Plus, manufacturers are using machine learning to improve product availability while decreasing any errors made in supply chain forecasting.


Machine learning is driven by data consumption, and there are so many manual processes used in healthcare. The good news is machine learning is helping to drastically improve workflows and the data compiled into electronic medical records.

With machine learning, healthcare practitioners can use the power of data-driven analytics. As a result, they are able toincreasetreatment options while optimizing medical resource efficiency.


In our modern times, we store most of our data on computers and various devices. Sensitive data could be saved on disks, local servers, or on the cloud. Moreover, massive amounts of data are transmitted every day across networks to other devices.

For these reasons and more, cybersecurity is critical in an age where cyber-attacks are growing by the second. It is welcome news that machine learning is helping to improve the cybersecurity industry.

Despite many advancements in cybersecurity technologies, there are still challenges to overcome. Machine learning is providing the answers to those obstacles. For example, machine learning algorithms can use models of behavior to make predictions about future security threats.

Final Thought

As you can see, there are many fantastic ways machine learning is used behind the scenes for multiple industries. If you notice an industry becoming much more efficient, then it's a safe bet it’s using machine learning.