Recently, interest in machine learning (ML) has increased dramatically. More and more people are becoming aware of the possibilities of new applications made possible by the machine learning approach. ML has paved the way for technological achievements and applications that would have been impossible a few years ago in the industry.
Machine learning is an umbrella term for several techniques and tools with which computers can learn and adapt themselves. Machine learning algorithms help AI learn without being explicitly programmed to perform the desired action. By learning a pattern from sample inputs, the machine learning algorithm predicts tasks and executes them based solely on the learned pattern and not on a predefined program instruction.
Image Recognition
Computer vision is one of the most exciting areas of machine learning. It is an approach for identifying and recognizing a feature or an object in the digital image. In addition, this technique can be used for further analyzes, such as B. Pattern recognition, face recognition, optical character recognition, and much more.
The computer vision algorithm describes the image content by comparing the characteristics of the images with the characteristics of the available samples. A bicycle photo is recognized since the information in the sample photo, on which the algorithm is trained, and the information in the input photo are correlated. The procedure of optical character recognition, also known as OCR, is the same as for general image recognition with the difference that the algorithm is trained on text content and its correlation with the visual representation of the text via fonts, sizes, formatting, and colors.
Sentiment Analysis
Sentiment analysis is another exciting field of application of machine learning. It deals with the way customers interact with a product and their attitude and opinion about the product. The system uses unsupervised machine learning to filter out emotions from a text as the primary recognition process. Intelligent algorithms can understand not only text and spoken language but even irony by analyzing the context. As an intelligent filter system, sentiment analysis can measure the customers’ feelings, identify the angry customers and sort them out for exceptional customer care so that they do not jump off.
Video Surveillance
A small video file contains more information than text documents and other media files such as audio and pictures. For this reason, extracting useful information from videos and automated video surveillance systems is becoming increasingly crucial for businesses. Monitoring multiple video cameras at the same time and gathering information about the presence of the same person in different video frames is not only very demanding, but it is also beyond human capabilities. In security-relevant areas, the identification of people using the videos is a critical point. The various ML algorithms can track people’s movements and identify them and sound the alarm in certain activities.
Email Classification And Spam Filtering
ML algorithms are also used to classify emails and automatically filter spam. There are many spam filtering approaches to email clients supported by ML to ensure that these spam filters are continuously updated. Over 325,000 malware attacks are detected every day, and each code is 90–98 percent similar to the previous versions. The safety programs supported by machine learning understand the coding pattern. Therefore, they detect new malware with a deviation of 2 to 10% and offer protection against it.
Virtual Assistant
Virtual personal assistants are software programs designed to naturally interact with an end-user, answer questions, follow a conversation, and perform various tasks. ML algorithms collect and refine information based on a user’s past behavior. This process helps tailor the results to the user profile. Siri, Alexa, Google Assistant are some of the most famous examples of virtual personal assistants. The accuracy, speed, and context capability of Alexa, Google Assistant, and Siri depend on the quality of the ML algorithms.
Product Recommendation
The recommendation of products and services, e.g., from Google or Amazon, is the forward-looking application of machine learning technology. Various machine learning methods with the inclusion of big data are used to develop this recommendation-based system. Anyone who buys a product online will receive emails with shopping suggestions or be informed about products that might interest them via advertising banners. Product recommendations are made based on customer behavior on certain websites, previous purchases, and items clicked on.
Social Media
Social media uses machine learning to introduce users, for example, to people they may know or suggest activities based on preferences, work, or place of study. Interest-based advertisements, suggestions from friends, recommendations, and personalized news feeds result from ML algorithms that scan user profiles, search through images and identify objects and behavioral patterns.
Fraud Detection
While the vast amounts of data available on the Internet are significant for data study and analysis, they also increase the likelihood of fraudulent activity. ML is becoming an effective technology for securing cyberspace. ML models are used to detect various types of online fraud, from detecting abnormal behavior to preventing money laundering. ML semantic analysis examines structured, unstructured, and tabular data to identify fake claims and messages. ML algorithms also search existing message repositories to find similar allegations and verify the authenticity of a message.
Dynamic Pricing
Organizations can use their historical price data and records of other variables to understand how specific dynamics – from time of day to weather to seasons – affect the demand for goods and services. Machine learning algorithms can learn from this information and combine this insight with the additional market and consumer data to help companies dynamically price their goods. This strategy ultimately helps companies maximize sales.
Medical Diagnosis
Machine learning includes several techniques and tools to solve diagnostic and prognostic problems in the various medical fields. ML algorithms are often used to analyze medical data to identify regularities in data, process data, interpret data, and effectively monitor patients. Machine learning also helps with disease outbreak estimation and prediction, processing medical information for research, planning and supporting therapies, and overall patient management.
Conclusion:
The adoption of machine learning is skyrocketing, and that is not surprising given its advantages, as manual tasks must be eliminated and valuable insights gained from data. In this article, we looked at specific machine learning use cases. However, these are just the most common examples of machine learning. Other notable mentions include financial analysis, machine translation, statistical arbitration, digital marketing, intelligent grid management, and many more.
The range of applications and use cases will undoubtedly expand in the next few years. Therefore, it will be interesting to see how and in which areas machine learning is used to improve efficiency, reduce costs, and achieve a better user experience.
Also Read : “Machine Learning” Has Arrived In Accounting
