Machine learning technology has become increasingly important and valuable in industries that deal with large amounts of data. Because machine learning derives insights from data in real-time, organisations that use it can work more efficiently and gain a competitive advantage.
Machine learning technology benefits every industry vertical in today’s fast-paced digital world. Here are the top five ML application sectors.
1. Healthcare Sector
Machine learning is becoming more popular in the healthcare industry, thanks to wearable devices and sensors like wearable fitness trackers, smart health watches, and so on. All of these devices track users’ health data in order to assess their health in real time.
Furthermore, technology is assisting medical practitioners in analysing trends or identifying events that may aid in better patient diagnosis and treatment. Medical experts can even use ML algorithms to predict the lifespan of a patient suffering from a fatal disease with increasing accuracy.
Furthermore, Machine Learning Is Making A Significant Contribution In Two Areas:
- Drug Discovery: Creating or discovering a new drug is an expensive and time-consuming process. Machine learning aids in the acceleration of the steps involved in such a multi-step process. Pfizer, for example, uses IBM Watson to analyse massive amounts of disparate data for drug discovery.
- Personalized Treatment: Drug manufacturers face a difficult challenge in validating the efficacy of a specific drug on a large population. This is because the drug only works on a small group of people in clinical trials and may cause side effects in some people.
Companies such as Genentech have collaborated with GNS Healthcare to address these issues by leveraging machine learning and simulation AI platforms, innovating biomedical treatments. ML technology searches for patients’ response markers by analysing individual genes, allowing patients to receive targeted therapies.
2. The Financial Sector
Several financial institutions and banks now use machine learning technology to combat fraud and extract critical insights from massive amounts of data. Insights derived from machine learning assist in identifying investment opportunities, allowing investors to decide when to trade.
Furthermore, data mining methods assist cyber-surveillance systems in detecting and neutralising warning signs of fraudulent activity. Several financial institutions have already collaborated with technology firms to reap the benefits of machine learning.
As an example,
Citibank has partnered with Feedzai, a fraud detection company, to combat online and in-person banking fraud.
PayPal employs a number of machine learning tools to distinguish between legitimate and fraudulent buyer-seller transactions.
3. Retail Industry
Machine learning is widely used on retail websites to recommend items based on users’ purchase history. Retailers use machine learning (ML) techniques to collect data, analyse it, and provide personalised shopping experiences to their customers. They also use machine learning for marketing campaigns, customer insights, merchandise planning, and price optimization.
According to Grand View Research, Inc.’s September 2021 report, the global recommendation engine market is expected to be worth $17.30 billion by 2028. Everyday examples of recommendation systems include:
When you shop on Amazon, the product recommendations you see on the homepage are generated by machine learning algorithms. Amazon employs artificial neural networks (ANN) to provide customers with intelligent, personalised recommendations based on their recent purchase history, comments, bookmarks, and other online activities.
Netflix and YouTube rely heavily on recommendation systems to provide users with shows and videos based on their viewing history.
Furthermore, retail websites are outfitted with virtual assistants or conversational chatbots that use machine learning (ML), natural language processing (NLP), and natural language understanding (NLU) to automate customer shopping experiences.
4. The Tourism Industry
Machine learning is helping to broaden the scope of the travel industry. Uber, Ola, and even self-driving cars use a powerful machine learning backend.
Consider Uber’s machine learning algorithm, which handles dynamic ride pricing. Uber manages dynamic pricing parameters with a machine learning model called ‘Geosurge.’ It employs real-time predictive modelling to analyse traffic patterns, supply, and demand. If you are running late for a meeting and need to book an Uber in a congested area, the dynamic pricing model takes effect, and you can get an Uber ride immediately but must pay twice the regular fare.
Furthermore, the travel industry analyses user reviews using machine learning. Sentiment analysis is used to categorise user comments based on positive or negative scores. Companies in the travel industry use this for campaign monitoring, brand monitoring, compliance monitoring, and so on.
5. Use Of Social Media
Machine learning allows billions of users to engage in social media networks more efficiently. From personalising news feeds to delivering user-specific ads, machine learning is critical in driving social media platforms. For example, Facebook’s auto-tagging feature uses image recognition to recognise and tag your friends’ faces. The social network employs ANN to identify familiar faces in users’ contact lists and to enable automated tagging.
Similarly, LinkedIn understands when you should apply for your next position, who you should connect with, and how your skills compare to peers. Machine learning enables all of these features.
