Common Machine Learning Applications You Should Know About
You heard about machine learning but you didn’t dig deeper because you think it does not concern you. This is where you are wrong. You have to know that machine learning applications are seen in your day-to-day life. Before learning about the applications, it is prudent to know the basics first.
Machine learning refers to a method of data analysis. It is actually a branch of AI (Artificial Intelligence) based on the concept that systems can learn from data, figure patterns and make decisions with minimal human intervention. Simply put, it focuses on the development of computer programs, which can access data and utilise it to learn. The process starts with a machine learning software.
To understand machine learning better, here are the common applications you should know about:
Image recognition is the most common machine learning application. Image recognition is beneficial for law enforcement and other agencies. It is mainly used for face detection and character recognition. In face detection, there are separate categories for every person in a database. In character recognition, machine learning can segment a piece of writing into smaller images.
SR (Speech Recognition)
SR is also called ASR (Automatic Speech Recognition) or STT (Speech to Text). Basically, this refers to the translation of spoken words into text. The software is designed to recognise spoken words. Machine learning can segment the speech signal into portions, which contain distinct phonemes or words.
In every segment, machine learning can represent the speech signal through the energy or intensities in various time-frequency bands. SR is applied in voice user interfaces like call routing and voice dialing.
Imagine there is a system that can compute the probability of any loan applications defaulting on loan repayments. To calculate the probability, the system will classify the available data into groups, which are set by the analysts. Once the classification is complete, the system can compute the probability.
For this application, machine learning is not only utilised by banks. It is also utilised by all sectors for different purposes. Through this, any business can take the required decisions on time.
IE (Information Extraction)
As the name suggests, IE is the process of extracting information from the unstructured data like webpages, articles, blogs, and emails. You already know that there is a massive volume of data generated and most of these are unstructured. Machine learning will convert the unstructured data to structured based on some patterns. If you have a business, you can now extract data in real time, not just End-of-Day.
Classification refers to the process of putting every individual from the population under study in different classes. These are called independent variables. Classification is helpful for analysts to establish an efficient rule. Machine learning can consider factors when assessing the customer’s ability.
Machine learning will provide techniques, methods, and tools that can help in diagnostic and prognostic problems in different medical fields. Machine learning is specifically used for the analysis of the clinical parameters and of the combinations for prognosis.
AI is everywhere and the possibility is endless. With machine learning, it helps in the analysis of massive quantities of data. At the end of the day, it will deliver faster and more accurate results regardless of your field or industry.