Why this course?

Description

HandE Learning is the largest Management and Degree Subject based E-learning institute of India with 200,000 students offers most comprehensive and practical internship programmes across more than 20 subjects in 100 plus topics or concepts. 
Collegiate Internship is the most Practical and Experiential Learning based programme includes complete subject learning, assignments and projects in different degree subjects, management and job function-based topics. This follows the complete guideline of UGC and NEP 2020 applicable to all the colleges and institutions across India. 

Collegiate is an IPO (Internship providing org) offers complete Internship for Undergraduate students in Online and Offline mode through various digital and tech labs in actual work situations in on-site experiential learning for real industry and research project in various subject for advance learning with application
1.The Internship is in actual work environment or digital/tech labs with outcome based learning and actual industry projects/assignments
2.Professional development through special sessions, exposure to digital and physical work environment through labs and industry environment 
3. Complete exposure to research aptitude by sharpening knowledge enhancement, analyzing, documentation, reporting and presenting. Exposure to emerging technologies in AI, ML, Analytics, digital tools
4.Entrepreneurial capabilities development by providing exposure and knowledge on how an organisation and business runs and required capabilities or functions to manage that.

The following structure for Internship would be followed
1.    Mentors and Supervisors would be allocated
2.    Mentors would be experienced people like research scientists, academicians, industry professionals, entrepreneurs and other experts, etc 
3.    All projects would be individual or few group projects
4.    Internship would be linked to the outcomes of value-added/skill- development/ability enhancement.

Check: https://www.collegiate.info/ | https://www.collegiate.info/subjects 

Description:

This course provides an opportunity for students to gain practical experience in applying Natural Language Processing techniques to real-world problems. The internship involves working on industry projects, enhancing NLP skills, and gaining valuable insights into the field.

Key Highlights:

• Real-world NLP projects
• Industry experience
• Enhanced NLP skills

What you will learn:

• Hands-on NLP experience
  Work on real industry projects to apply NLP techniques in practice.
• Skill Enhancement
  Enhance your NLP skills by working under the guidance of experienced professionals.
• Industry Insights
  Gain valuable insights into the application of NLP in various fields.
• Project Collaboration
  Collaborate with industry experts and peers to solve NLP challenges effectively.

 

Assignment: Introduction to Natural Language Processing
Objective: To assess students' understanding of fundamental Natural Language Processing (NLP) concepts and their ability to apply these concepts to real-world scenarios.

Project: Aspect-Based Sentiment Analysis for Product Reviews
Objective: The process involves gathering product reviews from sources like Amazon, Yelp, or e-commerce websites, preprocessing the data, extracting aspects like battery life and screen quality, performing sentiment analysis on each aspect, and creating an interactive dashboard to visualize the results, showing sentiment trends for each aspect.

Assignment 2: Introduction to Machine Translation and Language Generation
Objective: To assess students' understanding of Machine Translation and Language Generation concepts and their ability to analyze and implement these technologies.

Project: Fine-Tuning Transformer Models for Domain-Specific Text Classification
Objective: The project aims to explore the effectiveness of fine-tuning Transformer models, specifically BERT (Bidirectional Encoder Representations from Transformers), for domain-specific text classification tasks. The primary objective is to demonstrate how pre-trained Transformer models can be adapted and optimized for specialized domains, improving classification accuracy and adaptability.