Applications of Machine Learning in 2023
Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today’s leading companies, such as Facebook, Google, and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.
Healthcare, banking, e-commerce- artificial intelligence (AI), and machine learning are everywhere! This blog discusses fascinating and cutting-edge practical machine-learning applications in these domains.
1. Diagnosing diseases
ML healthcare One of healthcare’s main machine learning applications is identifying and diagnosing otherwise hard-to-diagnose diseases. Typical examples include various types of cancer that are tough to detect during the initial stages and genetic diseases.
For example, the now-defunct IBM Watson Genomics gave a very good glimpse at how combining genome-based tumor sequencing with cognitive computing can lead to a significantly faster diagnosis. Similarly, a leading biopharma company, Berg, has already deployed machine learning to develop therapeutic treatments in various domains, including oncology.
2. Image recognition
Image recognition is all around us. Notice the automatic scene and facial recognition feature in your smartphone’s gallery app? That’s done with machine learning. The same goes for social media features such as automatic friend tagging. Take Facebook’s Deep Face technology, which uses machine learning to offer suggestions to tag friends in pictures. State-of-the-art secret surveillance programs deployed by some world governments also rely on image recognition facilitated by machine learning.
Image recognition with machine learning also has applications in the healthcare sector. During many treatment procedures, missing even a tiny element could be devastating. Scans and X-ray pictures can use image detection to detect the tiniest of changes from one point to the other. This technique is particularly useful to accurately detect the progression of a virus infection or the spread of a tumor.
3. Autonomous vehicles
ML vehicles With autonomous vehicles, AI is expected to outperform a human driver, regardless of the scenario. And that’s precisely where machine learning algorithms come into play. Along with deep learning, machine learning algorithms allow autonomous vehicles to make decisions in real time.
The application of machine learning in self-driving cars like the Tesla Model S or Ford F-150 involves the onboard computer taking data input from a raft of sensors. This is to ensure that the ADAS can safely and accurately make sense of the world around the vehicle. The system uses this data stream to control the vehicle’s speed and direction. Additionally, the data is used to process other key activities like object detection and tracking, perception, and prediction.
4. Online fraud detection
Machine learning is increasingly useful in securing our online transactions and detecting forgery. It is especially effective at detecting fraudulent behavior involving the use of fake accounts, fake IDs, and phishing.
The application of machine learning in fraud detection involves algorithms trained with historical data to suggest risk rules. These rules can be implemented to block certain user actions that are flagged as suspicious — for example, suspicious logins, possible identity thefts, fraudulent transactions, and so on.
Usually, data scientists feed the machine learning model a huge volume of records of previous fraud and forgeries. They also flag non-fraud cases to eliminate false positives as much as possible.
5. Product recommendation
Product recommendations on e-commerce websites and entertainment platforms are prime examples of machine learning applications in business.
These recommendations are generated and delivered by “recommender systems.” These powerful engines leverage machine learning algorithms to segment customers as per their user data and behavioral patterns. User data and behavioral patterns are harvested from browsing history, likes, shares, and so on.
Market segmentation variables typically include:
- Behavioral: purchase patterns, usage rate, history, and loyalty status
- Demographic: age, gender, income
- Geographic: city, region, climate, etc
- Psychographic: interest, personality, lifestyle
Each consumer is served with personalized recommendations, content suggestions, and ads based on these variables.
6. Intelligent Virtual Assistant (IVA)
IVAs are machine learning-based systems capable of understanding natural language (read: human language) and responding accordingly. These intelligent AI assistants present a multiple-choice selection of answers to each question. They can also understand user intent from free text.
IVAs are increasingly finding their use in customer support automation, i.e., Virtual Customer Assistants. This allows users to articulate even highly specific concerns to the machine as they would explain to a human.
Note that IVAs are not the same thing as chatbots. They are much more sophisticated and capable of understanding natural language. In comparison, most chatbots communicate with users by offering simple yes/no types of options.
With the rise of no-code products, it is now possible to build IVAs in as little as 15 minutes.
7. Trading
Machine learning applications trading
Machine learning also has growing applications in stock and crypto trading. Sophisticated machine learning algorithms do the under-the-hood calculations and analyses. The data generated helps traders make informed decisions.
More specifically, machine learning comes in handy when it comes to extracting signals from financial and alternative data. These signals can then be used to design and backtest systematic strategies.
8. Machine Learning Applications in Healthcare
Doctors and medical practitioners will soon be able to predict how long patients with fatal diseases will live accurately. Medical machine learning systems will learn from data and help patients save money by skipping unnecessary tests. Machine learning algorithms will replace radiologists. McKinsey Global Institute estimates that using machine learning techniques to better and informed decision-making could generate up to $100 billion in value based on optimized innovation, enhanced efficiency of clinical trials, and the creation of various novel tools for physicians, insurers, and consumers. Computers and Robots cannot replace doctors or nurses. However, using life-saving technology (machine learning) can transform the healthcare domain. When we talk about the efficiency of machine learning technology, more data produces effective results – and the healthcare industry is residing in a data goldmine.
9. Uses for Machine Learning – Drug Discovery/Manufacturing
Manufacturing or discovering a new drug is an expensive and lengthy process as tens of millions of compounds must undergo a series of tests. Only a single one might result in a usable drug. Machine learning can speed up one or more of these steps in this lengthy multi-step process.
10. Machine Learning Applications in Cancer Prognosis and Prediction
The need to classify cancer patients into high or low-risk categories has prompted many research teams in the biomedical and bioinformatics fields to examine the use of machine learning (ML) technologies. As a result, these methodologies have been used to model the evolution and therapy of malignant illnesses.
Furthermore, the ability of ML algorithms to find essential characteristics in complex datasets demonstrates its significance. Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Bayesian Networks (BNs), and Decision Trees (DTs) are just a few of the approaches that have been widely used in cancer research to develop prediction models, resulting in effective and accurate decision-making.
Also Read: How To Become Better With ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING. In 10 Minutes
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