Top 16 real-life examples and use of Machine Learning
Machine learning is a modern innovation that helped people improve not only numerous industrial and professional processes but also their daily lives. Machine learning is now considered to be one of the most significant innovations since microchips. But what is machine learning? It’s a subset of artificial intelligence that uses statistical techniques to build smart computer systems to learn from its databases.
AI was once a fantastic science fiction concept, but now it becomes a daily reality. Some say AI leads to another industrial revolution. While the previous Industrial Revolution used physical and mechanical strength, the new revolution will exploit mental and cognitive abilities. But how? In this article, we will discuss 16 real-life examples of machine learning and how it helps create better technology to improve the quality of our lives.
1. Image Recognition
Image recognition is an everyday use of machine learning. Many situations can classify the object as a digital image. For example, the intensity of each pixel is used as one measure for a black and white image. Each pixel offers a 3-color intensity measurement in colored images, red, green, and blue (RGB). Machine learning can also be used in facial detection images. There is a separate category in multiple individual databases. Machine learning is also used to recognize handwritten and printed letters. We can segment a piece of writing into smaller, single-character images.
2. Speech Recognition
Recognizing speech is translating spoken words into text. Also called computer voice recognition or automatic speech recognition. An app can remember the words spoken in an audio clip or file and convert the audio to a text file. In this application, the measurement may be a set of numbers representing the voice signal. Intensity can also segment the voice signal in various time-frequency bands. Applications like voice-user interface, voice search, and more use speech recognition. Voice user interfaces include dialing, call routing, device control. Simple data entry and structured documents can also be used.
3. Videos Surveillance
Imagine single multi-camera monitoring! Indeed, a tedious and challenging task. Therefore, training computers to do this job makes sense. Today, before it occurs, the video surveillance system is AI-powered to detect crime. They track unusual behavior, such as long-standing still, stumbling or stumbling on benches, etc. Therefore, the system can alert people that can ultimately help prevent malfunctions. They help improve surveillance services when reported and counted as valid. This happens with a machine learning background.
4. Virtual Personal Assistants
Some of the famous examples of virtual personal assistants are Siri, Alexa, Google Now. As the name implies, when requested by voice, they help find information. Just activate them and ask, “What’s my schedule today?” What are German-to-London flights? “Or similar questions. Your personal assistant will look for information to answer your questions, recall your questions, or send an order to other resources (such as telephone apps) to collect information. You can even teach assistants for specific tasks such as ”Set an AM alarm next morning,” “Remind me to visit the Visa Office day after day.
5. Online Fraud Detection
Machine learning shows its ability to make cyberspace a safe place, and money fraud tracking is one of its examples. For example, Paypal uses ML to protect money-laundering. It uses a set of tools to compare millions of transactions, distinguishing between legitimate or illegal transactions between buyers and sellers.
6. Medical Diagnostics
Machine learning can be used in techniques and tools to diagnose diseases. It is used to analyze and combine clinical parameters to predict disease progression prediction, extract medical knowledge for research results, therapy planning, and patient surveillance. These are successful implementations of machine learning methods. It can help integrate computer systems in the healthcare sector.
7. Statistical Arbitrage
Finance arbitration refers to short-term automated trading strategies involving many securities. The user focuses on implementing a securities trading algorithm based on quantities such as history and global economic variables. Machine learning methods are used to achieve an index arbitration strategy. We apply linear regression and Vector Support at stock stream prices.
8. Learning Associations
Learning associations are the process of developing insights into different product associations. A good example is how to combine unrelated products. Associations between products that people buy are one of machine learning applications. When a person buys a product, similar products will be shown because the two products are related. When new products are launched, they are linked to old ones to increase sales.
Classification is a process of playing each individual in many classes. Classification helps analyze an object’s measurements to identify the object’s category. Analysts use data to establish productive relationships. For example, before a bank decides to distribute loans, it assesses customers ‘ ability to pay loans. We can do this considering factors like customer earnings, savings, and financial history. This information comes from previous loan data.
Prediction systems can also use machine learning. For example, a loan, the system needs to classify the available data into groups to calculate the probability of a fault. It is defined by regulations prescribed by analysts. Once the classification is done, the fault probability can be calculated. All sectors can calculate these calculations for various purposes. Prediction is one of the best machine learning applications. Let’s take traffic predictions, for another example.
We all used GPS navigation services. While we do that, our current locations and speeds are saved on a central traffic server. This data is then used to map the current traffic. While this helps prevent traffic and analyzes congestion, the underlying problem is that there are fewer GPS-equipped cars. Machine learning in such scenarios helps estimate regions where congestion can be found on a daily basis.
Extracting information (IE) is another machine learning application. This process removes structured data from unstructured data. Web pages, articles, blogs, corporate reports, etc. The relational database maintains information extraction output. As a set of documents, the extraction process generates structured data. This output is summarized in a database relationship, like an excel sheet and table. Extraction is now a critical factor in big data. We know the vast volume of data generated, most of which are unstructured. The first significant challenge is managing unstructured data. Convert unstructured data into a structured, pattern-based form to be stored in RDBMS. The data collection mechanism also changes in the current days. We’ve collected data in bats like End-of-Day (EOD) in the past, but now businesses want to collect data in real-time as it’s generated.
Machine learning can also be implemented in regression. Regression allows us to use machine learning to optimize parameters. It can also be used to reduce the approximation error and calculate the nearest result. We can also use machine-learning to maximize functionality. We can also choose to change inputs to achieve the closest possible outcome.
13. Financial Services
Machine learning in finance and banking has great potential. It’s the driving force behind the popularity of financial services. Machine learning can help banks and financial institutions make smarter decisions. Machine training can help financial services detect closure before it happens. It can also track spending patterns for customers. Machine learning can also perform market analysis. Train smart machines to monitor spending patterns. The algorithms can quickly identify and react in real-time.
14. Online Customer Support
Several websites now offer opportunities to chat with customer support representatives while browsing the site. Not all websites have a live executive to answer your questions. You’re talking to a chatbot mostly. These bots tend to extract and present information to website customers. Meanwhile, the chatbots progress over time. By their machine learning algorithms, they are more likely to understand user queries and provide better answers.
15. Spam and Malware Filtering
Several email clients use spam filtering. To continually update these spam filters, they use machine learning. Upon spam filtering, the latest tricks adopted by spammers can not be tracked. Multi-Layer Perceptron, C 4.5 Tree Inducing Decision are some of the ML-powered spam filtering techniques. More than 325 000 malware is detected daily, and each piece of code is 90–98 percent similar to its previous versions. Machine learning security programs understand the coding pattern. With a 2–10% variation, they easily detect and protect new malware.
16. Product Recommendations
You bought a product online a few days ago; then you receive emails for shopping suggestions. If not, you may have noticed the shopping website or app suggests items suit your taste. This refines your shopping experience, of course, but did you know it’s a machine teach you magic? Product recommendations are made based on your website/app behavior, past purchases, items liked or added to a cart, brand preferences, etc.
In short, machine learning is an incredible breakthrough in artificial intelligence. And while machine learning has a terrible impact, these machine learning applications are one way to improve our lives.
This article was originally published on roboticsBiz. Republished with permission