Best Machine Learning Applications with Examples

Machine learning is the leading technology for artificial intelligence and big data implementation. Its use is exceptionally broad: whenever businesses implement? innovative technology, they usually consider ML as well, because this is a reliable way of assuring algorithms continuous improvement.

Netflix, for instance, was able to save more than 1 billion dollars each year due to its powerful ML-algorithms that lie in the core of the personalized content catalog. The selection system is continuously improving with no human aid, offering a better customer experience every day.

Google designed a machine learning system that learned to build AI better than top companies developers. The technology is a long-term resource-saver since it improves automatically and constantly delivers better results.

Characteristics of Machine Learning

Machine learning is powered by insights. The pace and quality of algorithms improvements rely not as much on computing power or impeccable code, as rather, on high-quality data. If the company knows how to collect information from its users, monitor on-site interactions, and listen to social media posts, they can achieve remarkable results – as shown by Google, Amazon, Netflix, Microsoft, Facebook.?

Let’s take a look at machine learning applications?in multiple industries, from digital businesses to industries that are still transforming to understand the benefits of this technology.?

Image recognition

With machine learning, the software can be trained to detect objects and features on images. The neural network analyzes a ready library of images pixel by pixel. Each neuron offers insight after validating their piece of content, and the network unites millions of these conclusions into a cohesive analysis.

Developers use an open image database to teach transfer learning systems to recognize these images. The biggest datasets for ML image recognition are Coco and ImageNet.

Examples of machine learning in image recognition

• Clearview – a facial recognition technology that analyzes data from social media to get insights on people’s faces and get their data. Despite many privacy controversies, the app is often used for crime prevention and police work.?
• Deepomatic – a computer vision software that allows businesses to deploy image recognition AI in their software.?
• Visenze – an online e-commerce platform that allows customers to upload pictures of favorite products and look for similar items in stock.

Social media analysis

Machine learning can analyze millions of posts on Facebook, Twitter, Instagram, read comments, and personal updates. Businesses keep up with customer feedback, track their brand health, and improve reputation.?

Machine learning allows systems not only to recognize words but understand the context behind them. For instance, “orange” can be used to describe a color or a name brand. As an owner, you’ll want to see the posts that fall under the second category – and ML makes it possible by analyzing the context of a message.?

Emotional recognition

AI and ML analyze the context in which the brand’s name is used and compare a single message to millions of similar posts. Complex algorithms track the differences between happy, unhappy, interested, or sarcastic.

Unsupervised detection

Businesses use machine learning to know what their customers care about. Machine learning isn’t limited to pre-set values: in other words, it will show all results that fit the general context, not just what the business owner searched for. It’s possible to get unexpected results that fall out of your expectations.?

Applications of machine learning for social media analysis

• Lionbridge: this sentiment-analysis tool provides users with insights, based on social media posts in more than 300 languages.?
• Scale AI: data analysis company that processes information from social media, online searches, posts, databases, using AI and ML algorithms;
• Monkey Learn: a text classification software where users can analyze the text without being limited to social media posts only – although it’s often used for social listening.

Smart assistants

Smart assistants are looking for new ways to interact with users and provide a better experience. They need to analyze personal data, process voice requests, and automate daily tasks. Without a powerful learning system, the assistant will not be able to adapt to changing user needs.?

With Machine Learning, a home assistant can process and categorize all input data, and possibly reuse it later. With natural language processing, an assistant collects and analyzes voice messages from users, finds logical relationships between phrases, and understands what output corresponds to the request.

Machine learning use cases in the industry

Machine learning allows the smart assistant to use all collected data to improve their pattern recognition skills and be able to address new needs. Amazon constantly refines machine learning algorithms for Alexa.

The main ML method, used by the system, is active learning – the system uses data to learn new skills automatically, but if it’s impossible, the software will identify areas where the help from human professionals is required. This way, development teams are always aware of the tool’s blind spots and know where to step in.?

Additionally, scientists introduced a new ML method – transfer learning, and Amazon implemented it in Alexa. Developers can reuse skills from one domain to help machines acquire competencies in a different field. If the system knows how to find restaurants, it will quickly pick up the skill of finding bars and supermarkets.?

Healthcare online services

Determining a diagnosis, planning treatments, managing follow-up appointments, and keeping track of patient’s progress can be significantly simplified with machine learning. The treatment process is influenced by hundreds of factors, and doctors can’t always take all of them into account.

The technology finds patterns between patient’s scans, history, symptoms. Also, it determines the precise diagnosis, taking into account hundreds of factors, many of which could be easily missed by human medical professionals.

Sure, there are plenty of challenges in the way of implementing machine learning into healthcare regularly. Technology needs to be well-tested and refined before it can take responsibility for diagnosing and treating patients. This can only be possible if medical and development expertise are brought together in continuous collaboration.?

Machine Learning business applications in healthcare

• Kensci: predictive healthcare platform that alerts patients and medical professionals about likely health issues and helps to prevent infections and disease complications.?
• PathAI: a machine learning diagnosis platform that analyzes patients histories and scans for making accurate predictions on the disease origin and symptoms.?
• InnerEye – a platform developed by Microsoft, designed to distinguish between tumors and healthy tissues for precise cancer diagnosis.

Age/gender identification

With the rise of social media and video content, organizations and businesses are interested in identifying people age and gender from their pictures, style of posts and messages, as well as voices. Machine learning technologies for age and gender identification have multiple applications in fields such as law, access control, security, and others.

Examples of age and gender analysis

• Quividi: a facial recognition application that uses Artificial Intelligence and Machine Learning to analyze online users’ faces and provide precise insights for targeted ads.
• AgeBot – an Android application that provides users with an estimate of their age and gender based on pictures, videos, and real-time images from a smartphone camera.

If you're looking for reasons to implement machine learning in your industry, check out the full article on the Jelvix blog.