Machine learning is ubiquitous in our everyday life. 

Whether or not you know it, the odds are that machine learning powers the applications you use daily. It’s the underlying technology behind many apps on your smartphones, from Siri to virtual assistants to Google Maps and more. 

Businesses also use AI and machine learning to automate processes, gain insights through data analysis, and engage with customers and employees. To top that, organizations leveraging machine learning are seeing a quantum leap in revenue. 

Take Netflix, for example. Netflix uses machine learning to predict what shows its customers will want to watch next, based on their searches and watch history. Their recommendation engine saves the company a staggering $1 billion each year. 

But what exactly is machine learning? And how does it differ from AI?

In this blog post, I will dive into what machine learning is and its various types, along with how it differs from AI and how organizations are leveraging it to boost results. 

What Is Machine Learning?

Machine learning (ML) is the science of computer algorithms that let computerized systems learn without complex programming. 

Machine learning is based on algorithms. Algorithms are a set of rules that govern specific behavior. For example, a developer might write an algorithm for a robot that says, “When your sensor detects an object 10 cm in front of you, stop.”

Algorithms form the basis of every computing system. They can get overly complex, but ultimately it all boils down to “If X behavior happens, take Y action.”

How does this relate to machine learning?

Machine learning is the ability of machines (computers, phones, and other systems) to take an algorithm, improve it, and learn over time as they interact with more data. 

Let’s go back to our robot example. Suppose the robot was a self-driving vehicle. Stopping if it detects a person is great. But what if it sees a bump ahead? Or detects something small, like a leaf? Does it still need to stop? Obviously not.

With machine learning, you can feed your system with terabytes of data. It learns the differences and writes its own algorithms based on the underlying programming to achieve the desired results. In a nutshell, machine learning is the process of teaching a computer system how to make accurate predictions based on the data provided. 

Types of Machine Learning

There are three subcategories of machine learning:

• Supervised machine learning
• Unsupervised machine learning
• Reinforcement machine learning

Let’s take a closer look at these:

Supervised Machine Learning

A supervised machine learning model teaches machines by example. The algorithms learn from labeled data sets to generate expected predictions. 

For example, in a house price prediction, we first need to collect data about houses, like location, house size, number of rooms, square feet, etc. Next, we need to collect price data for these houses. With data from thousands of homes, including their features and prices, we can train a supervised machine learning model to predict a new house’s price based on the available data. 

Unsupervised Machine Learning

In an unsupervised machine learning model, a program looks for patterns in unlabeled data. 

These models can look for patterns and trends in data that are hard to find manually. For example, an unsupervised machine learning program scans online sales for a given period and identifies unique buying habits that people are not explicitly looking for. 

Reinforcement Machine Learning

This sub-category of machine learning uses its own experiences to learn.

The algorithm discovers and analyzes data through trial and error and decides what action results in higher rewards. For example, reinforcement learning can train a self-driving vehicle to drive by telling the machine when it made the right decision. This helps it learn over time and document what actions it should take in the future. 

What’s the Difference Between AI and Machine Learning?

AI and machine learning are often used interchangeably. 

However, AI is a broader concept of simulating human thinking, while machine learning is just one of the mechanisms to achieve AI. In theory, one can create AI using traditional programming, but that will require a super-complex algorithm. 

Machine learning, on the other hand, leverages statistical techniques, essentially a form of applied math to achieve the same result. AI has several branches such as computer vision, robotics, NLP, and, more recently, explainable AI. 

How Can Businesses Leverage Machine Learning to Boost Results?

With the explosive growth of data and big data analytics, organizations are capitalizing on improving their processes by harnessing the power of machine learning.

Machine learning has taken the center stage because it’s the technology that enables businesses to gain insights from raw data. This technology is quickly becoming ubiquitous across all industries, from medical research to agriculture, the stock market, and more. I would argue that within the next decade, businesses that don’t leverage machine learning will cease to exist, very similar to companies that didn’t adopt digitization a couple of decades ago. 

Harnessing the power of machine learning enables businesses to:

• Improve business operations by making data-driven decisions
• Adapt to the ever-changing market conditions
• Gain valuable insights into the consumer needs and the overall business

For example, suppose an online retailer captures user data within their website. Using machine learning, the store owner can analyze and extract patterns, statistics, information, and stories within the data. They can then use this information to make reliable and informed business decisions. 

Apart from this, machine learning solutions are evolving to execute more complex tasks, thus replacing tedious tasks for humans, freeing their skillset for more important decision-making at hand. AI and machine learning solutions have the advantage in that they can perform jobs faster and more efficiently. 

Wrapping Up

Machine learning is the process of teaching machines and computer systems how to make accurate predictions based on the data provided. Harnessing the power of machine learning enables businesses to gain valuable insights into their customers’ needs, adapt to the ever-changing market conditions, and improve operations.