The most popular use cases of machine learning: Automation, Cybersecurity, Digital marketing, and Digital Communication
The last two years have been years of inactivity for people worldwide. One of the positives that we have seen in the last two years is the sudden surge in machine learning applications. Machine learning applications have rapidly contributed to the advancement of technology and industry in a time when human beings were unable to do so. Machine learning has greatly contributed to various dimensions of artificial intelligence and has helped in conceiving advanced statistical models to achieve specified tasks. Use cases of machine learning have made it clear that this field is going to be of pivotal importance to humans in the coming times. Reacting to the rapid advancement in artificial intelligence, people have preferred certification in machine learning as one of the important means of securing their place in the upcoming job market. In this article, we take a look at various use cases of machine learning that have become extremely popular in the last two years.
The degree of automation
The degree of automation that we have witnessed in the last two years can be seen as a consequence of machine learning techniques and algorithms. For instance, the customer support mechanisms of various companies that functioned with the help of trained professionals in the past are now functioning with the help of chatbots. Equipped with great natural language capabilities, chatbots help in online customer interaction and substantially reduce the burden on support staff. Advanced chatbots also take up inquiries and satisfy the customers with spontaneous responses. The customer grievance redressal mechanism has been transformed with the help of chatbot services. In the present time, chatbots not only provide customers with useful answers but also provide them with recommendations related to various products.
The core of cybersecurity
As the sensitive data of customers that is related to their personal details as well as transactions is sourced through the data pipeline, it becomes extremely important to secure this information. The increasing complexity of networks and online processes has also increased the levels of cyber threats in the present times. In addition to this, the ecosystem of the internet of things has virtually put public data into rapid motion. The emerging threat and hacking mechanisms make it necessary to devise ways and means for improving cyber security. It is in this context that machine learning techniques and algorithms have enabled us to come up with cyber security models that can safeguard public data.
The domain of digital marketing
The domain of digital marketing has witnessed a large-scale expansion in the present times. For the last two years, there has been a significant increase in the digital activity of customers and this has acted as a positive factor for E-Commerce firms. Needless to mention, other domains of digital activity like online platforms and software applications have also regained a lot of focus in the times of the pandemic.
The companies significantly utilize customer data to get insights into purchasing habits, product recommendations, marketing information, and other details of customers that were largely unexplored. Advancements in machine learning have made it possible to attract new customers into the domain of digital marketing. In addition to this, the companies have also been able to engineer new methods of digital marketing with the help of machine learning algorithms. These include personalized recommendations, targeted campaigns as well as predictive analytics.
Digital communication
The process of digital communication has witnessed significant transformation with the help of machine learning algorithms. Machine learning has given way to Natural Language Processing, speech recognition as well as language translation. All this has given a new lease of life to digital connectivity platforms and communication mechanisms like videoconferencing and email correspondence. Machine learning has also led to the development of regional connectivity applications and has connected people on the basis of their location, age, and digital activity. These applications can be compared to traditional communication platforms like Microsoft Clippy. However, the distinguishing feature of the new communication platforms is that they are conceived with the help of machine learning capabilities.
The way ahead
In the future, the use cases of machine learning would increase further as the dimensions of digital platforms continue to expand. This means that digital access will need to be improved to ensure digital inclusion as well as digital correspondence. Hence, machine learning can act as a significant factor for the software development, development of digital marketing and the design of smart products and applications in the coming times.
