How NLP Becomes Suitable For The Mass-Market

Modernization has changed our lives significantly, leading to changing needs and demands. It impacts businesses as they need to update and upgrade their products and services based on market trends. It is challenging to keep track of changing consumer demands and continue launching suitable products and services. Thus, businesses keep looking for technologies, tools, methodologies, and strategies to enhance customer and market understanding and grow their business successfully.

The industrial revolution has brought remarkable transformations in our everyday life and work. Emerging technologies are enhancing our capabilities for efficient and effective business performance. These technologies work on the data collected from various sources to extract meaningful, hidden, helpful information from structured and unstructured data. Natural Language Processing, Artificial Intelligence, and Machine Learning are some of the trending technologies in the digital world. 

Natural Language Processing (NLP) is computer-aided human language processing, including written and spoken language. It is used to either understand the language or generate the language. Artificial Intelligence and Machine Learning are used to analyze and extract meaning from the vast amount of collected data. Businesses collect data for various aspects of the company, like processing, operation, product design, technology, and marketing. NLP is suitable for companies targeting mass-market if it has:

• Access to vast amounts of market data
• Experts to develop cutting-edge technologies
• Computing capacity to process the collected data

How is NLP Suitable for the Mass Market?

NLP is used to translate, generate, and understand language and emotions used in various formats like speech, text, audio, and video. Marketing has been one of the most important criteria for a successful business. Hence the suitability of NLP for marketing can be understood by its applications for business marketing, as discussed below:

• Sequence classification: It assigns the text sequence to one of multiple previously defined classes of information. For example, information may provide emotions like joy, anger, sadness, sorrow, etc. The marketing team can determine the emotions behind feedback, review, and discussions about the product/ services on social media. Based on this information, the team can create strategies and plans to improve its sales and make the product satisfy consumer needs and demands.
  
• Question-Answer models: It gives a content-correct answer to questions based on information available from the text corpora. The marketing team can use it to automate responses on chatbots or over customer service calls. 
• Text generation: It matches the word based on given text to predict another word or lengthy text accurately. It enables text generation for multiple languages based on the target group and their linguistic dialect. 
• Phrase recognition assigns one or more words in a sentence to a class, also called Named Entity Recognition (NER). 

Future of NLP Technology

NLP is the most researched domain in the artificial intelligence field. It will have advancements with more profound understanding and developments. In addition, there will be general-purpose and specialized models for language processing helpful for a wide range of businesses. The field will evolve rapidly, and thus demand for experts, professionals, and NLP technology career aspirants will increase drastically.

The latest developments in NLP technology are fast and impressive, and universities worldwide include this technology in syllabi. In addition, various training and education institutes such as Imarticus Learning Pvt. Ltd. are providing certification programs in this field to generate next-generation professionals.

Artificial Intelligence and Machine Learning Programs

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All of these courses are designed with the help of the E&ICT Academy, industry leaders, and IIT Guwahati to train aspiring machine learning certification program learners. It will help you to become Data Scientist, Machine Learning Engineer, Data Analyst, Machine Learning Architect, Data Science/ Machine Learning Consultant, and AI Engineer.    

This course develops fundamental skills and a practical understanding of diverse industries' theoretical backgrounds and real-world projects. You can learn job-relevant skills with 25 in-class real-world projects under the guidance of world-class academic professors. The course includes:

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