Machine learning is so prevalent today that you probably use it many times a day unknowingly. However, as a Machine learning engineer, you need to be aware of some popular Machine learning use cases. Before moving on to discuss Machine learning use cases, let’s skim through the two types of machine learning, which are supervised learning and unsupervised learning.
Supervised Learning
Supervised learning is a type of machine learning that uses labeled datasets. These labeled data sets are designed to supervise or train algorithms to predict outcomes accurately or classify data. In supervised learning, you can learn about the machine learning model over time, and its accuracy can be measured using these labeled inputs and outputs.
There are two types of Supervised learning as regression and classification.
Unsupervised Learning
Unsupervised learning is building machine learning models to cluster and analyze unlabeled data sets. These models are called “unsupervised” as we can use them to discover hidden patterns in datasets without any human intervention.
There are three main applications of unsupervised learning: dimensionality reduction, association, and clustering.
Machine learning is a buzzing technology in the market nowadays. , In the future, it will play a major role in transforming our lives by bringing more innovations to the table and opening doors for new opportunities. You can learn more about machine learning and its future trends by referring to this article.
Now it’s time to discover ten exciting machine learning use cases:
1. Chatbots
Developing chatbots is one of the popular Machine Learning use cases. These chatbots have currently become the machine-versions of virtual assistants. Natural Language Processing (NLP) and Machine Learning facilitate building more sophisticated chatbots.
When talking about some well-known existing chatbots, Facebook uses Messenger as its chatbot platform. Another popular chatbot is Google Assistant. Jokebot is a type of non-assistant chatbot which is widely used for entertainment. The Socialbot is another application of chatbots that can promote a specific candidate, issue, service, or product.
The latest machine learning-based chatbot technology uses evolutionary algorithms that can communicate based on the uniqueness of each communication held. As technology is becoming more complex, developers now focus on making chatbots more independent by using machine learning and intent-based algorithms.
Nowadays, chatbots are more advanced, and they are gaining the capability to work automatically. So they will be able to figure out real-time needs and deliver the best performance.
2. Fraud detection
Machine learning models can detect hidden correlations between the probability of fraudulent actions and user behavior by processing large datasets with many variables. They require less manual work and process data faster compared to rule-based systems. Supervised learning is mainly used to detect fraud.
Some leading financial institutions use machine learning systems to combat fraudsters. For instance, MasterCard has integrated machine learning to process and track variables, including purchase data, transaction device, time, location, and size.
The machine learning model can assess the behavior of the account in every operation and provides a real-time conclusion on whether a transaction is fraudulent. Therefore, it reduces the number of false declines in the payments. Since these false declines are one of the largest possible areas for fraud, modern machine learning-enabled technology has helped prevent such fraud, which is a strategic goal of financial institutions and banking industries.
Feedzai, a fintech company, has claimed that a machine learning model can detect up to 95% of all the fraud. Capgemini claims that the accuracy of detection in machine learning-based fraud detection systems has been improved by 90%.
3. Robotic process automation
Robotic process automation (RPA) is a machine learning-based technology that automates business processes using software robots. There, robots perform repetitive and tedious tasks with nearly 100% accuracy. Supervised learning is the type of machine learning used for robotic process automation.
Machine learning-based RPAs can draw conclusions, comprehend, and analyze both unstructured and structured data. This intelligent RPA effectively uses data to learn from them continuously and act on them. These robots are always capable of making smart decisions.
Furthermore, we can use them to automate processes, especially when a large amount of data needs to be structured, compared, analyzed, or processed. Robotic process automation is widely used for document information extraction, speech recognition, and image recognition.
For instance, many accountants receive invoices from their suppliers daily with notifications of payments. This process can be simply automated with the help of intelligent RPA so that it will take only a few seconds per invoice. Most importantly, it will be free of human errors.
4. Direct Marketing
Machine learning-based marketing models help clients to make better decisions on marketing investments to maximize profitability. Machine learning models excel in their predictive performance and capability to identify the worst and best candidates for direct email campaigns. So, clients can use them to optimize direct email campaigns to maximize the combination of key performance indicators (KPIs) or any individual KPI. Supervised learning is mainly used for direct marketing.
Machine learning also helps to improve response rates by 20%-30% or more by optimizing databases and lists. A newly built machine learning model can also use the data derived from a previously built model to generate a higher return of investment (ROI).
Moreover, machine learning models can be used to identify additional opportunities to generate revenues by enhancing your cross-sell/upsell efforts. Win-back machine learning models can identify previous customers previous customers who have the highest probability of converting back when there is a series of offers or a compelling offer.
Check out this article to get an idea of how to use machine learning to enhance the efficiency and productivity of your business.
5. Personalized Content
Businesses widely use machine learning to personalize content. Offering personalized content can increase sales, brand value as well as customer engagement. Unsupervised learning is mainly used to personalize content.
Machine learning models allow you to identify what kind of content persuades people to take action. Another advancement of machine learning is the capability to create dynamic content recommendations for different segments of customers. That is what Netflix does. Their machine learning system can alter the artwork based on what customers have watched in the past.
Netflix uses machine learning to identify the top ten recommendations for user households. So, they can offer videos for each member of the household based on their interest. They also ask their members to provide their valuable feedback, which is useful for improving their machine learning models and offering their members the best-personalized content.
Netflix has claimed that 75% of the videos users watch on Netflix come from machine learning-based personalized recommendations.
6. Early Stroke Diagnosis
Early stroke diagnosis is another valuable use case of machine learning. The sudden death of brain cells requires immediate treatment and diagnosis. According to statistics, if patients receive professional help within three hours after the first symptoms, they can recover faster. Supervised learning is mainly used for early stroke diagnosis.
Intracerebral hemorrhage (ICH) is the deadliest type of stroke, with hard disabilities in most survivors and 40% mortality. Data scientists from Geisinger have created a machine learning model to detect the signs of intracerebral hemorrhage (ICH) using over 46,000 CT scans of the brain. They tested this machine learning model for three months and implemented it into routine care. It has decreased diagnostic time by 96%. Researchers have also found that this model is capable of spotting subtle symptoms of ICH that are missed by radiologists.
Machine learning is also successfully applied to diagnose ischemic stroke caused by LVO or large vessel occlusion. According to Google’s Teachable Machine, trained machine learning models have correctly identified this type of stroke in 77.4% of cases.
7. Cardiac Risk Assessment
Heart disease is considered the major cause of death among men and women worldwide. Machine learning-based cardiac risk assessment using ECGs has significantly prevented heart attacks and decreased mortality. Supervised learning is mainly used for cardiac risk assessment.
Machine learning models obtain a large amount of data for learning from more than 300 million ECGs carried out each year globally. Studies have proven that machine learning not only predicts future risks of heart diseases but also spots current abnormalities of ECGs.
For instance, MIT developed RiskCardio technology in 2019 to predict the probability of death caused by cardiovascular disease within 30 to 365 days for patients who have already recovered and survived acute coronary syndrome (ACS).
Geisinger Medical Center researchers have used over two million ECGs for training to build machine learning models that predict who is at higher risk of dying within a year. Those machine learning models are capable of identifying risk patterns overlooked by cardiologists.
8. Diabetic Retinopathy Screening
Another application of Machine learning is retina image analysis in the field of ophthalmology. There, Machine learning helps to detect diabetic retinopathy (DR), an eye complication that leads to strokes and blindness in one of three patients with diabetes So early detection is crucial to prevent vision loss of those patients. Supervised learning is mainly used to detect diabetic retinopathy.
IBM launched its machine learning model in 2017 to detect diabetic retinopathy and classify its severity from mild to proliferative status. This model has provided 86% accuracy.
However, Google outperformed this result by creating a model in collaboration with Verily, who was training machine learning models for three years. For that model, they used a dataset of 128,000 retinal images.
Google’s AI Eye Doctor has achieved 98.6% accuracy, on par with human experts at detecting DR. Now, this model is used at Aravind Eye Hospital in India to assist doctors. These types of machine learning-based systems have greatly reduced the burden of ophthalmologists and eye technicians.
9. Lung Cancer Screening
You may have heard of lung cancer as the world’s deadliest oncology disease. As it leads to cancer-related mortality, early detection is a must for saving lives from this disease. Again, Supervised learning plays a vital role in detecting lung cancer.
In 2019, Google built a machine learning model in collaboration with Northwestern Medicine by training 42,000 chest CT scans. That model delivered the best performance, and it was able to diagnose lung cancer better than radiologists with eight years of expertise. It was also capable of detecting malignant lung modes 5 to 9.5% more often than medical experts.
Another machine learning model has proven that it can predict spot chronic obstructive pulmonary disease (COPD), which leads to cancer.
Since machine learning models are now available to assist radiologists in analyzing large numbers of CT images, the survival rate of many lung cancer patients will increase, and they will get access to successful treatments.
10. Early Melanoma Detection
Skin cancer is considered the world’s most common malignancy. It hits 20% of the population by the age of 70. Luckily, 99% of the patients can recover if they are treated on time by early detection. Machine learning has been widely applied for visual pattern recognition of skin cancers without relying on dermatologists and radiologists. Supervised learning is mainly used for early melanoma detection.
Scientists from Stanford University built a machine learning model in 2017 by training 130,000 clinical images of skin pathologies to detect skin cancers. That model has provided the accuracy demonstrated by medical experts and dermatologists.
In 2018, the European Society for Medical Oncology (ESMO) was able to build another model that provided even better results. That model correctly detected melanomas and offered 95% accuracy, while the accuracy of dermatologists was 86.6%.
In March 2020, researchers from Seoul National University built a machine learning model to predict malignancy using over 220,000 images. It provided a significant performance and was capable of classifying 134 skin disorders.
Conclusion
Remember that machine learning is not magic. Machine learning models are advanced math-based algorithms that learn from data and can be used to identify patterns. When properly applied to the right use cases, machine learning can reduce the time you have to allocate for error-prone manual technical operations. That will also highly reduce technology costs and add significant value to your businesses.
