Due to the advancement of technology in recent years, machine learning has come to the forefront of the artificial intelligence world. Machine learning has given artificial intelligence the capability to understand the world and seemingly operate with minimal human intervention. Today’s machine learning is nothing like it was about a decade or two ago. I mean, we have self-driving cars that operate almost entirely without someone holding the wheel!

What is Machine Learning (ML)?

Machine learning is actually a method of data analysis that works by allowing systems to learn from data, look for patterns, and make a predictive decision. 

A good example of machine learning is Amazon’s Alexa device. You can ask Alexa to play any music you want. Over time, the data you’re giving Alex will help the AI determine what type of genre you like. When you say, “Alexa, play music,” it will play music from that genre. Alexa has spotted a pattern and will create a playlist that it thinks you will enjoy. 

Of course, machine learning in AI goes beyond just a simple home device. Artificial Intelligence is used in health and business industries, as well as many others. They can help automate and make processes more efficient.

Difference Between AI and ML

Are AI and machine learning the same thing? This is often confusing for many people.

Artificial Intelligence (AI) is a science that involves studying ways of building intelligent programs and machines. These highly designed programs and machines are able to solve problems that humans typically solve. But, not all AIs are the same. Some AIs are much more intelligent than others. For example, IBM Watson is much more intelligent than, say, your traditional chatbot. In 2011, IBM Watson won first place in a Jeopardy game against two former champions. 

Machine learning is actually a subset of artificial intelligence. It grants AI the ability to learn and make decisions using data. This happens without any explicit programming in the systems. This means that an AI will always learn from previous experience as time goes by to make better recommendations/decisions than before.

Machine Learning Methods

Today, we use a few machine learning methods. They have supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Supervised and unsupervised learning are the two most widely used machine learning methods. Let’s dive a bit deeper into how these methods work.

Supervised Learning

Supervised learning trains machines by using pre-defined sets of data. The machine is trained using a predefined set of training examples, such as knowing what kind of fruit will be classified as a banana-based on what it observed the last time it underwent this test. It’s seen the data before and knows what it is labeled as. The end goal of using supervised learning is for the machine to make accurate and correct recommendations eventually. 

Unsupervised Learning

Unsupervised learning is the opposite of supervised learning. Unlike supervised learning, the machine is not given pre-defined sets of data. Instead, it is given some data, and it must find any patterns and relationships within those pieces of data. It is trying to determine what type of data is being shown to it without any previous knowledge of it. Think of it like learning, but without a teacher there to help or guide you, you have to figure it out all on your own.

Semi-Supervised Learning

In this machine learning method, the machine is trained using a combination of “labeled” and “unlabeled” data. This combination usually uses more unlabeled data than labeled data. Semi-supervised learning works by letting the machine cluster unlabeled data together based on factors, such as how similar they are to each other. From there, it uses the existing labeled data to label the rest of the unlabeled data. Think of it as filling in the gap. 

Reinforcement Learning

Reinforcement learning is a goal-oriented machine learning method. The machine will learn to identify the suitable action that it must take to maximize a reward it can earn in a specific situation. For example, say it is playing a game of Pac-Man. Over time, it will learn that eating more white dots lets it reach higher numbers, which leads to a higher score. It will attempt to improve on this process to reach the highest score it can attain.

Benefits of Using ML in Retail

Machine learning can provide many benefits to a retail business. Some of the best benefits are:

• Customer Service: There may be times where you are not able to provide customer service. This is where machine learning can be of great assistance. A chatbot or other customer service AI can assist customers without the use of a person. It can learn to better help customers over time through algorithms from a machine learning method. This means that your retail business will always be available to help your customers, which can greatly improve your customer experience.

• Product Recommendations: A website or app can help give product recommendations to customers. Consider the many times you browse through an online store, and it recommends a list of items. Machine learning makes this process work by observing what you are looking at and what you are buying. From there, it can recommend similar products to you.

• Predict Sales: You can also use machine learning to help you predict how sales might be during a certain sale or promotion. Using predictive analytics, machine learning can analyze what products do well in a sale or what sales perform the best. 

These are just some of the benefits a retail business can have when implementing machine learning into its operations.

Conclusion

Machine learning has advanced a lot in recent years. This advancement has given us the ability to create and automate more efficient processes using different machine learning methods. There are many ways you can use machine learning to benefit your business.

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