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Computer Vision & NLP
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Course Mode
Offline
Duration
4
Eligibility
For a B.Tech program: A candidate must have passed 10+2 with Physics, Chemistry, and Mathematics (PCM) and qualify in a relevant entrance exam. For an M.Tech program: A candidate must have a B.E. or B.Tech degree in Computer Science, IT, or a related field with a valid GATE score or a university-level entrance exam score.
Entrance Exam
JEE Main & JEE Advanced, WBJEE, GATE,
Type of Course
UG
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Course Summary

Computer Vision (CV) is a field of AI that trains computers to interpret and understand the visual world. It involves techniques for image and video processing, object detection, and facial recognition. The curriculum focuses on algorithms and models (like CNNs) that allow machines to see and analyze images.

Natural Language Processing (NLP), on the other hand, is the AI discipline that enables computers to understand, interpret, and generate human language. The course covers topics like text analysis, sentiment analysis, machine translation, and the development of chatbots. Both specializations are highly technical and require a strong background in mathematics and programming.

📅 Upcoming Admission Deadlines

  • computer-vision-nlp with 50% scholarship August 28, 2026

Top Recruiters

TCS
Wipro
Infosys
Accenture
Amazon
Microsoft
IBM

Career Scope

Software Developer

College-wise Fees

Frequently Asked Questions

Computer Vision enables computers to "see" and interpret the visual world by identifying patterns in images and videos.
These fields are often combined to improve machine understanding. For example, a system could use computer vision to identify objects in a scene, while NLP could interpret text labels or user voice commands to provide context and guide the system's actions.
Healthcare: Combining image analysis (computer vision) with doctor's notes (NLP) can improve patient care and diagnostics. Autonomous Systems: A self-driving car might use computer vision to detect traffic signs and use NLP to process spoken navigation commands from the driver. Smart Assistants: Voice assistants use NLP to understand spoken questions and can then use computer vision to analyze images or videos the user provides.
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