Natural Language Processing is an interdisciplinary field that uses computational methods. To investigate the properties of written human language and to model the cognitive mechanisms underlying the understanding and production of written language.
To develop novel practical applications involving the intelligent processing of written human language by computer. In addition to central theoretical foundations, courses on mathematics and computer science, the programme imparts problem-solving and practical competence.
Natural Language Processing (PGDNLP) Highlight
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Course Level |
Postgraduate |
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Full-Form |
Bachelor of Technology in Information Technology |
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Duration |
1 year |
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Eligibility |
Minimum 50% in graduate with Physics, Chemistry, and Mathematics as the main subjects. |
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Admission Process |
Direct Admission or on the basis of the Entrance test. |
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Course Fee |
INR 30,000 to INR 8,00,000 |
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Average Salary |
INR 3,00,000 - INR 4,50,000 |
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Job Positions |
IT Analyst, Software Developer, System Engineer, Programmer |
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Top Recruiting Areas |
IT sectors, Banks, Hospitals, and Educational Institutes. |
Eligibility Criteria for Natural Language Processing (PGDNLP)
1. Candidates must have passed the Class 10+2 exam from a recognized board with Physics, Chemistry, and Mathematics as core subjects.
2. Candidates must have passed Class graduation with a minimum of 45-50% of marks. Some reputed colleges conduct entrance exams for admissions to their colleges.
3. Admission to the program is done through a nationwide entrance test.
Career after Natural Language Processing (PGDNLP)
IT professionals are very high in the job sector. So, those who are looking for a creative and challenging work environment may opt for a job in the startup sector. One may also use IT skills and other skills related to computing and programming to start their own venture and help in nation-building.
There are many multinational companies (regarding the IT sector) that are recruiting candidates from the colleges/universities' campuses.
This course encompasses all the relevant NLP topics, including text, classification, tagging, parsing, machine translation, semantic, discourse analysis, and Hidden Markov Models, among other things.
Web Developer and Designer
Data Security Officer
Database Manager
Software Developer
Information Technology Engineer
