| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Intermediate |
| Duration | 4 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹5499 / $82 |
| Tools | Python NLTK spaCy Hugging Face Transformers Gensim BERT GPT |
About the Natural Language Processing (NLP) Course
This Program is designed to provide a comprehensive understanding of Natural Language Processing (NLP) techniques and applications. Participants will explore the foundational principles of NLP, including text preprocessing, tokenization, and sentiment analysis.
The course will delve into advanced topics such as topic modeling, sequence models, and state-of-the-art transformer models like BERT. By the end of the course, participants will be proficient in using key NLP libraries and frameworks, preparing them for advanced studies or careers in NLP and AI.
Program Highlights
• Comprehensive coverage of Natural Language Processing (NLP) 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 Processing (NLP)
- Overview and historical evolution of Natural Language Processing (NLP)
- 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 Processing (NLP)
- 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 Processing (NLP)
- 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 Processing (NLP) 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 Processing (NLP) 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 Processing (NLP) 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
