What is Machine Learning? Explained With Examples

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Machine learning- while it is the latest buzz around, we know how complex it is. It has become a topic of discussion everywhere you go from conferences to events, and talks centered on technological innovations and threats. The field of machine learning has been around for quite some time now, but it is in the last decade that it has truly emerged around the commoners. The day when Arthur L. Samuel created the first machine learning application that played chess, it was established that the machines would play a significant role in human lives in the near future. 

The emergence of machine learning paved the way for machines that did not have to be explicitly programmed, rather, can learn from experience and interactions just as a human world. This has become the main approach towards creating intelligent machines and programs where machine learning algorithms allow the machines to learn by itself. 

We know it all sounds overwhelming as to how a machine can make decisions and learn by itself just with human interaction. With that said, we are going to dive deep into the subject and help you understand everything you need to know about machine learning, including what it is, some real-life examples, latest trends, how businesses can use it, and much more. 

Let’s get started. 

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So, what is Machine Learning ? – Machine Learning is the craft of having machines, computers make decisions without humans providing commands or instructions, allowing them to pattern match complex situations and forecast things. 

If you broaden your mind a little, you will see that machine learning is a simple idea. Machines are built around algorithms. You can define algorithms as a set(s) of rules that control specific action or behavior. For instance, you can create an algorithm for a robot that says, “Stop when the camera detects any object in front of you.”  

Algorithms are the key to computing, and they can get exceedingly multifaceted. However, the fundamentals remain the same, which is “Learning = Representation + Evaluation + Optimization.” In simple terms, it all boils down to ‘in case of X situation, do Y actions.’

So, machine learning is when a computer takes an algorithm, improves it, and learns over time, as it communicates with more data and processes information.  

Now, considering the robot example mentioned above, imagine that algorithm being implemented in a self-driving vehicle. Stopping the car if it detects a living thing in front of it is commendable. But, what if it detects a plastic bottle or says a twig? Does the car still need to stop? 

Machine learning allows programmers to feed machines and computers with tons of data, ensuring that they can learn to tell the difference, and change the algorithms based on the principal human-driven approach, thereby achieving the desired result.  

With that said, the easiest definition of machine learning would be “the practice of using algorithms to analyze data, learn from it, and then take prediction or determination about something in the world.” 

The prediction could be answering whether the fruit in a photo is an apple or papaya, whether the use of the word ‘book’ in a sentence relates to a hotel reservation or a paperback, spotting people crossing the road in a self-driving car, or determining whether an email is spam. 

The critical difference between a machine learning algorithm and traditional computer software is that computer software isn’t taught how to reliably discriminate between fruits. 

Some Incredible Examples of Machine Learning in Practice 

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Now that you have had your basics cleared about machine learning, let’s see how it is influencing businesses, people, and our everyday lives. Here are some amazing practical examples of machine learning being implemented in real life. 

#1 Consumer Goods 

• Coca Cola is the largest beverage company in the world, with over 500 drink brands sold in 200 countries. That means the company creates a lot of data and therefore, it has embraced new technology to put that data into practice for new product(s) development. They have capitalized on machine learning and AI and are testing AR (augmented reality) in their bottling plants as well.
• Heineken has been a brewing leader for the last 150 years. The company is looking to boost their success by the massive amount of data they collect. From the Internet of Things to data-driven marketing and improving operations using data analytics, they use data and AI augmentation for improving their advertising, marketing, operations, and customer service. 
• Hello Barbie is a popular children’s doll for girls. It uses natural language processing, advanced analytics, and machine learning to listen and respond to a child. It works in a way that the microphone on Barbie’s necklace records and transmits what is said to the servers at ToyTalk. The response is analyzed, and an appropriate response from 8000 lines of dialogue is determined. 

#2 Financial Services

• American Express processes over $1 trillion in transaction. It has over 110 million AmEx cars in operation. Thus, the company relies heavily on machine learning algorithms to help identify fraud in real-time. They have developed apps by leveraging the data flows to connect a cardholder with services or products and special offers. 
• Credit reference agency Experian collects its extraordinary amount of data from public information records, transactional records, and marketing databases. With machine learning, they are customizing their products to allow for more effective and quicker decision-making. 

#3 Healthcare 

• Infervision, an AI high-tech company is using machine learning to save lives! Since China is experiencing a scarcity of radiologists to keep up with the demand of reviewing approximately 1.4 billion CT scans every year to look for the signs of lung cancer, Infervision trained algorithms to supplement the work of radiologists, allowing them to diagnose cancer more efficiently and accurately.   
• Google’s DeepMind can mimic the thought process of the human brain. Inspired by neuroscience, DeepMind has magnificently crushed humans at games, what’s interesting is that it has been used in healthcare applications to help use machines in diagnosing ailments and reduce the time taken to plan treatments. 

How are businesses using machine learning?

Business applications of machine learning have only emerged over the last five years. But since it has established its roots, there are numerous ways businesses are using machine learning. It includes: 

• Smarter Hiring where machine learning is allowing employers to screen resumes and cover letters for identifying top candidates for the job post
• Image Recognition where machine learning is being used to train computers for reading medical scans, recognize specific products from pictures on the internet, and tag the right face on social media 
• Financial Services where machine learning is being used to identify trends, track the spending patterns, perform market analysis, and detect fraud as well 

Final Words 

Machine learning is going to be an essential asset of the future because its predictive power will disrupt several industries. Machine learning is a great way to turn data into valuable insights. Seeing the sudden surge in the application of machine learning, it can be said that progress is likely to continue at a rapid pace. What this means is there are going to be a lot of opportunities for individuals who are looking to progress their career in the field of analytics and data science.