Businesses often need to analyze highly subjective data such as customer feedback, reviews, and recommendations to aid in their brand decision-making. But simply automating data analysis leads to the nuances of this data being overlooked. Sentiment analysis with Machine Learning (ML) models provides a more comprehensive solution to this problem.
User experience (UX) designers rely on a variety of research methods and analytics to understand what the end-user prefers in the design of a software solution or product. Ethnographic research is a tool with great potential that UX designers are finding to be extremely insightful in discovering user preferences and pain points regarding product design.
Automotive brands need to make their customer journey more personalized and innovative to maintain a competitive edge in the market. Artificial Intelligence (AI)-based chatbots have become the go-to technological innovation for these brands to make the end-user experience a notch above the rest.
According to popular UX statistics, people form their judgment on a website’s credibility purely on its aesthetics. In fact, 88% of the users are unlikely to get back to a website or any digital platform after a bad user experience.
Referring to some of the most pivotal technologies as mere buzzwords is a disservice to innovation. The Internet of Things (IoT) is one such domain of technologies most people tend to refer to, without fully understanding its nuances or its technical aspects.
Artificial Intelligence is resolving real-world, complex challenges at scale. With its transformative capabilities, AI has managed to enter some of the sensitive areas including healthcare, finance, cyber security, etc.
When handling voluminous data that is highly sensitive, it is always preferable to group it into categories or classes. That is primarily where classification algorithms make themselves useful. Classification algorithms are one of the most widely implemented classes of supervised machine learning algorithms.
There are Yottabytes of sensitive data being generated from the interfacing of humans with machines. For cost-effective and optimal enrichment of this data, Machine Learning (ML) algorithms are our best bet. One of the most reliable categories of ML algorithms is clustering algorithms, irrespective of the complexity of data.
The exponential growth and development that AI technology has showcased in the past two decades have brought several science fiction movies into reality. Statistics suggest that the global AI market is predicted to reach $190 billion by 2025. The overall market includes a wide array of AI applications including Natural Language Processing, Machine Learning, Conversational AI, Robotic Process Automation, Neural Networking, etc. | Source: Statista