An introduction to Machine Learning and 5 key benefits

Machine learning (ML) is a key technology within the field of Artificial Intelligence (AI).

In this article, you'll get an introduction to machine learning and learn about 5 key benefits.

What is Machine Learning?

According to IBM, “Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.”

In other words, machine learning is a subset of AI that helps machines learn in more human ways. Using machine learning, systems can learn new information after being "trained" on data sets. Over time, the systems get "smarter". 

Using machine learning, systems can also make more intelligent decisions and predictions when faced with novel circumstances.

5 benefits of machine learning

“The goal of machine learning is to adapt to new data independently and make decisions and recommendations based on thousands of calculations and analyses.”


1. It identifies potentially profitable opportunities for your business

Machine learning can processes big data sets and uncover novel relationships in your data. It can also be used to make predictions based on your data. These capabilities can sometimes uncover previously undiscovered growth opportunities for your business.

2. It helps guide the decision-making processes of your business

With its abilities of analyzing data and predicting future outcomes, machine learning can help guide your business decision making in more quantitative ways. 

Using data in decision making will keep your business heading in the right direction, optimizing your current and future operational efforts and making it easier to produce insights that can be quickly acted upon in the best interests of the company.”

   David Robins (CEO of Binfire Corp)

Decision-making can be made easier with machine learning.

3.  It reveals potential risks

Assessing and prioritizing risks is extremely important to ensure the adequate focus of the management on critical areas.”

      Vishal Ruia (EY India, Partner Risk Consulting)

By utilizing machine learning, your company can better assess and properly handle risks. 

4. It automates processes

ML can allow companies to automate complex and error-prone manual processes, leading to massive gains in efficiency and accuracy. 

In the words of Dave Waters, “Machine learning will automate jobs that most people thought could only be done by people.”

5.  It can improve customer experience

57% of enterprise executives believe that the most important growth benefit of AI and machine learning will be improving customer experiences and support.


Customer service remains a pivotal part of every business no matter what the size or industry. ML may dramatically transform the customer service experience in the near-term.

Learn more about machine learning below...

Types of Machine Learning

There are three types of machine learning: Supervised, unsupervised, and reinforcement learning.

1. Supervised Learning

In a supervised learning model, the algorithm learns based on a labeled dataset and generates predictions based on past experiences in order to respond to new data.

For example, imagine we were trying to predict pricing for flight tickets for new routes. First, we would need to gather data about existing flights, such as features and amenities. Then, we would need to find out the prices of those flights on their respective routes.

Now, with that labeled dataset, we could train a supervised ML model to predict the prices of flight tickets for new routes.

2. Unsupervised Learning

Unlike the supervised learning model, the algorithm learns on an unlabeled dataset. In this case, the algorithm will be fed data and the necessary tools to understand the properties of that data.

From there, the algorithm can learn to group or organize the data in a manner that allows humans or intelligent algorithms to make sense of it.

Unsupervised learning is interesting because a vast majority of data in today’s world is unlabeled. It gives us a powerful tool to make sense of the world. 

Unsupervised learning can be broken down into different types. These include clustering, anomaly detection, association, auto-encoders, latent variable models, and Neural Networks.

3. Reinforcement Learning

When it comes to reinforcement learning, the machine learning model learns how to behave in a certain environment by taking actions and analyzing reactions.  

This model is learns via trial and error. That means it reinforces positive outputs and discourages negative ones.

Imagine a baby learning how to walk. If the baby is able to walk into the open and safe arms of a caregiver, then the baby is happy. That’s a positive response. 

However, if the baby falls down and doesn't reach a caregiver, that leads to a negative experience.

How does this relate to reinforcement learning?

The baby tries different methods to get to a caregiver, analyzes the results of its actions, and learns the best way to get a reward. 

Real-World Applications of Machine Learning

Here are some real-world examples of machine learning:

  1. Automatic Language Translation in Google Translate
  2. Smartphone with face recognition
  3. Driverless/Self-driving car
  4. Netflix Recommendation System
  5. Weather Prediction


ML use cases in business

According to Industry Research, the Global Machine Learning market is projected to grow by USD11.16 billion between 2020 and 2024, progressing at a Compound Annual Growth Rate (CAGR) of 39% during the forecast period.

ML is growing across the world. Now, let’s talk about how various industries are using ML.

1. Credit default rate

Using ML, financial institutions can build models to assess risk, score credit cards, and make more accurate predictions about consumers.

2.  E-commerce

E-commerce is experiencing rapid growth as people shop online. ML plays a pivotal role for vendors by helping them track their customer needs, monitor trends, predict the behavior of their customers, and more.

Have you ever bought items from an online shopping platform, only to start seeing recommendations for similar items on your social media timeline? That’s ML at work, performing "retargeting" of ads.  

3.  Transportation

One key factor for operating a delivery/ logistics service is efficiency. ML makes it possible to optimize carrier selection, rating, routes, and other processes. This helps to save money and improve efficiency.

4.  Fraud detection

ML models can dramatically improve the customer verification process that predicts fraud in real-time. Lenders can use ML models to train historical application data to uncover relationships between application variables and the likelihood of fraud.

Other industries utilizing ML include cyber security, manufacturing, media, and many others.

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