The terms “Deep Learning” and “Machine Learning” usually go hand in hand, and many people are often confused with these interchangeable buzzwords. But it is essential to know the difference between them. You can find various examples of deep learning and machine learning in your routine life.
Do you know how a customer support staff knows if their service will give you a better experience before you even join a survey, what is making self-driving vehicles real, how Netflix recommends a show that you will like the most, and how Facebook knows whose picture it is in your profile? If your answer is Artificial Intelligence, then how does it do so smoothly?
In simple words, Deep Learning is an advanced form of machine learning which helps machines to make the right choices without human intervention. If you say deep learning is actually Machine Learning, you wouldn’t be wrong.
So, what is machine learning?
For better understanding, here is a simple definition –
“Machine Learning is a group of algorithms that analyze data, learn from it, and apply their learning to make knowledgeable decisions.”
Automated Transport Management Systems is one of the best examples of machine learning. This technology uses machine learning to detect speed and traffic violations for law enforcement authorities.
A common and basic example is a music streaming platform that decides which artists or new songs a listener would like to stream on-demand. The algorithms associate the user’s taste with other users who share the same preference. This technique is also known as Artificial Intelligence which is used in many fields.
Machine learning consists of complex coding. From data security companies that provide antivirus solutions to kill malware, adware, and different types of computer viruses, to financial institutions that are aimed to send alerts for ideal trades, machine learning is everywhere. The algorithms have been programmed to learn constantly.
Machine learning consists of complex coding and math that performs mechanical functions at the end in a way that a motor vehicle, a flashlight, or a TV does. It performs according to the data provided to it, and it gets better with time. For example, you have a flashlight. You say “it’s dark,” and it turns on automatically. So, it would track the word “dark” in various phrases.
When the machine goes deeper to learn new tricks, it is known as deep neural networks and deep learning.
Deep Learning vs. Machine Learning
Deep learning is simply a part of machine learning which works the same way. But their capabilities are different. This is the reason why most people are often confused with machine learning. Though basic ML models update their normal function with time, they still need something to tell how to do it. If it is given a wrong prediction, then someone has to make changes. On the other side, a deep learning algorithm determines and chooses the accurate prediction on its own using its neural network.
For better understanding, let’s retake the previous example of the flashlight. If you say “dark,” the flashlight considers it as an audible cue, and it turns on. If it keeps learning, it might automatically turn on with any sentence with the word “dark.” If the deep learning model has been put into practice, the flashlight will work if it hears cues like “light switch is not working” or “Can’t see anything.” It has its own brain or computing power to learn with time.
How Do Deep Learning and Machine Learning improve Customer Service?
Machine Learning is used in most modern AI applications. For example, they can help improve reliability in workflows, improve staff productivity, and boost self-service. They continuously receive customer queries, and this data is fed into their algorithms, including the most common issues that customers have. As a result, aggregating it into the AI can help in making accurate and faster predictions. This way, an AI can be an excellent prospect for a lot of businesses.