Machine learning is a modern innovation that has enhanced many industrial and professional processes as well as our daily lives. It’s a subset of artificial intelligence (AI), which focuses on using statistical techniques to build intelligent computer systems to learn from available databases.
With machine learning, computer systems can take all the customer data and utilise it. It operates on what’s been programmed while also adjusting to new conditions or changes. Algorithms adapt to data, developing behaviours that were not programmed in advance.
Learning to read and recognise context means a digital assistant could scan emails and extract the essential information. Inherent in this learning is the ability to make predictions about future customer behaviours. This helps you understand your customers more intimately and not just be responsive, but proactive.
Machine learning is relevant in many fields, industries, and has the capability to grow over time. Here are six real-life examples of how machine learning is being used.
Image recognition is a well-known and widespread example of machine learning in the real world. It can identify an object as a digital image, based on the intensity of the pixels in black and white images or colour images.
Real-world examples of image recognition:
Machine learning is also frequently used for facial recognition within an image. Using a database of people, the system can identify commonalities and match them to faces. This is often used in law enforcement.
Machine learning can translate speech into text. Certain software applications can convert live voice and recorded speech into a text file. The speech can be segmented by intensities on time-frequency bands as well.
Real-world examples of speech recognition:
Some of the most common uses of speech recognition software are devices like Google Home or Amazon Alexa.
Machine learning can help with the diagnosis of diseases. Many physicians use chatbots with speech recognition capabilities to discern patterns in symptoms.
Real-world examples for medical diagnosis:
In the case of rare diseases, the joint use of facial recognition software and machine learning helps scan patient photos and identify phenotypes that correlate with rare genetic diseases.
Arbitrage is an automated trading strategy that’s used in finance to manage a large volume of securities. The strategy uses a trading algorithm to analyse a set of securities using economic variables and correlations.
Real-world examples of statistical arbitrage:
Machine learning optimises the arbitrage strategy to enhance results.
Machine learning can classify available data into groups, which are then defined by rules set by analysts. When the classification is complete, the analysts can calculate the probability of a fault.
Real-world examples of predictive analytics:
Predictive analytics is one of the most promising examples of machine learning. It's applicable for everything; from product development to real estate pricing.
Machine learning can extract structured information from unstructured data. Organisations amass huge volumes of data from customers. A machine learning algorithm automates the process of annotating datasets for predictive analytics tools.
Real-world examples of extraction:
Typically, these processes are tedious. But machine learning can track and extract information to obtain billions of data samples.
Machine learning is a remarkable technology in the field of artificial intelligence. Even in its earliest uses, machine learning has already improved our daily lives and the future..
If you're ready to apply machine learning to your business strategy and create customised experiences, check out the Personalisation Builder. Use the power of predictive analytics and modelling to understand each customer’s preferences!