What is Machine Learning? What are the Uses of Machine Learning?

One of the topics on the agenda of the digitalized world, whose popularity has increased in recent years, is machine learning, that is, machine learning. What is machine learning, which is an important concept in terms of banking and artificial intelligence technologies and offers many advantages to the banking sector?

What is Machine Learning?

Machine learning, which can be defined as a kind of application in which computer programs can learn patterns through training data and algorithms, is a sub-branch of artificial intelligence. The application, which imitates human movements, aims to learn through experience, without programming. Thanks to training data and algorithms, it detects data and automatically completes tasks by making predictions.

Artificial intelligence machine learning, first used by IBM researcher Arthur Samuel in 1959, forms the basis of applications such as Google Assistant and Siri used today. Machine learning, which is considered as a sub-branch of artificial intelligence, enables the computer to think like a human and perform its tasks on its own.

In order for the computer to think like a human, a neural network consisting of algorithms modeled on the basis of the human brain is used.

What are the Uses of Machine Learning?

In today's world, where technology is developing and the digitalization process is spreading rapidly, machine learning applications can be used in almost every field. You can encounter machine learning in many areas, especially online shopping, social media applications, banking and finance sector, health and education. In order to get to know the usage areas of machine learning better, we have listed a few examples for you:

• ASR (Automatic Speech Recognition): Designed by utilizing NLP technology (link can be linked to NLP content) to convert human voices to text, ASR enables voice calls to be made from mobile devices or the conversations to reach the other party in the form of messages.
• Customer Service: Online conversation robots designed for customer communication are one of the most applied areas of machine learning. Online conversation robots can answer frequently asked questions by customers and provide personalized advice to users. Messaging robots, virtual and voice assistants on e-commerce sites are good examples of machine learning use.

What is Deep Learning?

Deep learning, which is considered a sub-branch of machine learning, is a technique that creates patterns using algorithms and huge datasets and gives appropriate answers to these patterns, without human intervention. Data scientists often use deep learning software to analyze large and complex data, perform complex tasks, and respond to images, text, and audio faster than humans.

Deep learning technique teaches devices to filter, classify and make predictions from audio, text or image inputs. Thanks to deep learning, smart home devices can understand and apply voice commands, and autonomous vehicles can distinguish pedestrians from other objects. The deep learning technique uses a programmable neural network so that machines have the ability to make correct decisions without the human factor. Deep learning, the usage area of ​​which is increasing day by day; He has a voice in many fields such as voice and face recognition systems, vehicle autopilots, driverless vehicles, alarm systems, health sector, image improvement, and cyber threat analysis.

What are the Differences Between Machine Learning and Deep Learning?

Although the concepts of machine learning and deep learning are often used interchangeably, they have different properties. The main difference is the amount of data processed. Small amounts of data are sufficient to make predictions in machine learning. In deep learning, huge amounts of data are needed to develop predictive ability. Accordingly, there is no need for high computational power in machine learning, whereas many matrix multiplication operations are used in deep learning technique.

For machine learning skill acquisition, features need to be defined and created by users. In deep learning technique, features are learned from data and new features are created by the system itself. Output in machine learning; while it consists of numerical values ​​such as classification or score, in deep learning technique the output is; may differ in the form of text, audio or score.