| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Intermediate |
| Duration | 3 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹4249 / $56 |
| Tools | Python NLTK spaCy Hugging Face Transformers Gensim BERT GPT |
About the Natural Language Generation (NLG) Course
This program offers a comprehensive exploration of NLG techniques, teaching participants how AI can automatically generate coherent and contextually accurate human language.
The program covers language models, neural architectures, ethical considerations, and hands-on projects focused on implementing NLG in real-world applications like automated writing, chatbots, and content creation.
Program Highlights
• Comprehensive coverage of Natural Language Generation (NLG) from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Natural Language Processing
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Exposure to industry-standard tools and platforms used in Natural Language Processing
• Career-oriented training for academic and professional growth in Natural Language Processing
Course Curriculum
Module 1: Introduction to Natural Language Generation (NLG)
- Overview and historical evolution of Natural Language Generation (NLG)
- Key terminology, definitions, and core concepts in Natural Language Processing
- Current industry landscape, trends, and career opportunities
- Setting up the learning environment and essential tools
Module 2: Fundamentals and Theoretical Foundations
- Core principles and scientific/theoretical underpinnings of Natural Language Generation (NLG)
- Mathematical and analytical frameworks relevant to Natural Language Processing
- Comparative analysis of major approaches and methodologies
- Understanding key standards, guidelines, and best practices
Module 3: Text Processing
- Introduction to Text Processing concepts and methodologies
- Step-by-step practical implementation of Text Processing techniques
- Tools and platforms commonly used for Text Processing
- Troubleshooting, optimization, and best practices
Module 4: Sentiment Analysis
- Introduction to Sentiment Analysis concepts and methodologies
- Step-by-step practical implementation of Sentiment Analysis techniques
- Tools and platforms commonly used for Sentiment Analysis
- Troubleshooting, optimization, and best practices
Module 5: Named Entity Recognition
- Introduction to Named Entity Recognition concepts and methodologies
- Step-by-step practical implementation of Named Entity Recognition techniques
- Tools and platforms commonly used for Named Entity Recognition
- Troubleshooting, optimization, and best practices
Module 6: Advanced Topics and Emerging Trends in Natural Language Processing
- Cutting-edge research and innovations in Natural Language Generation (NLG)
- Integration with AI, automation, and modern technologies
- Industry case studies and real-world problem solving
- Future directions and career pathways in Natural Language Processing
Module 7: Capstone Project and Assessment
- End-to-end project implementation using Natural Language Generation (NLG) skills
- Peer review, collaborative exercises, and expert feedback
- Portfolio-ready project documentation and presentation
- Final assessment and course completion evaluation
Tools, Techniques, or Platforms Covered
Python NLTK spaCy Hugging Face Transformers Gensim BERT GPT
Real-World Applications
- Apply Natural Language Generation (NLG) skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Natural Language Processing competencies
- Solve industry-relevant problems using Natural Language Generation (NLG) methodologies and tools
- Contribute to open-source projects and collaborative research in Natural Language Processing
- Prepare for competitive examinations, interviews, and professional certifications in Natural Language Processing
Who Should Attend & Prerequisites
- Students pursuing degrees in Natural Language Processing, science, engineering, or related disciplines
- Working professionals seeking to upskill or transition into Natural Language Processing roles
- Researchers and academicians looking to adopt modern techniques in Natural Language Processing
- Entrepreneurs, freelancers, and self-learners interested in practical Natural Language Processing knowledge
Certification
