What does the Natural Language Understanding course cover?
Natural Language Understanding is covered here in 14 modules: Introduction to Natural Language Understanding: What is NLU? : Definition and scope of NLU, Text Preprocessing: Text Cleaning : Removing noise and irrelevant data, Part-of-Speech Tagging: Introduction to POS Tagging : Definition and importance and 11 more. The outline lists 44 specific topics, opening with What is NLU?
How do you approach Natural Language Understanding step by step?
The work is sequenced in 14 stages. It starts with Introduction to Natural Language Understanding: What is NLU? : Definition and scope of NLU, moves through Text Preprocessing: Text Cleaning : Removing noise and irrelevant data and Part-of-Speech Tagging: Introduction to POS Tagging : Definition and importance, and ends at Practical Tools and Techniques for Self-Assessment.
What is in Module 1 of the Natural Language Understanding course?
Module 1 is Introduction to Natural Language Understanding: What is NLU? : Definition and scope of NLU. It works through What is NLU? : Definition and scope of NLU, History of NLU : Evolution and milestones in NLU, Applications of NLU : Real-world applications and use cases and 1 more. It sets the vocabulary the remaining 13 modules build on.
How is the Natural Language Understanding course delivered?
The Natural Language Understanding course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.
How much does the Natural Language Understanding course cost?
The Natural Language Understanding course is $199 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.
Closely related courses: Natural language understanding Toolkit, Natural Language Understanding in Business Process, Natural Language Understanding in AI Risks Kit, Natural Language Understanding and AI innovation Kit.
More answers: what you get with every course, refund policy, all help answers.
Natural Language Understanding: A Complete Guide and Practical Tools for Self-Assessment
Course Overview
This comprehensive course provides a thorough understanding of Natural Language Understanding (NLU) and its applications in the real world. Participants will gain hands-on experience with practical tools and techniques for self-assessment, and upon completion, receive a certificate issued by The Art of Service.Course Features
- Interactive and engaging learning experience
- Comprehensive and up-to-date content
- Personalized learning approach
- Practical and real-world applications
- High-quality content and expert instructors
- Certificate issued by The Art of Service upon completion
- Flexible learning schedule and user-friendly interface
- Mobile-accessible and community-driven
- Actionable insights and hands-on projects
- Bite-sized lessons and lifetime access
- Gamification and progress tracking
Course Outline
Module 1. Introduction to Natural Language Understanding: What is NLU? : Definition and scope of NLU
- What is NLU?: Definition and scope of NLU
- History of NLU: Evolution and milestones in NLU
- Applications of NLU: Real-world applications and use cases
- Challenges in NLU: Limitations and challenges in NLU
Module 2. Text Preprocessing: Text Cleaning : Removing noise and irrelevant data
- Text Cleaning: Removing noise and irrelevant data
- Tokenization: Breaking down text into individual words
- Stopwords: Removing common words like he, and, etc.
- Stemming and Lemmatization: Reducing words to their base form
Module 3. Part-of-Speech Tagging: Introduction to POS Tagging : Definition and importance
- Introduction to POS Tagging: Definition and importance
- POS Tagging Techniques: Rule-based and machine learning approaches
- POS Tagging Applications: Sentiment analysis and information extraction
Module 4. Named Entity Recognition: Introduction to NER : Definition and importance
- Introduction to NER: Definition and importance
- NER Techniques: Rule-based and machine learning approaches
- NER Applications: Information extraction and sentiment analysis
Module 5. Sentiment Analysis: Introduction to : Definition and importance
- Introduction to Sentiment Analysis: Definition and importance
- Sentiment Analysis Techniques: Rule-based and machine learning approaches
- Sentiment Analysis Applications: Customer feedback and opinion mining
Module 6. Dependency Parsing: Introduction to : Definition and importance
- Introduction to Dependency Parsing: Definition and importance
- Dependency Parsing Techniques: Transition-based and graph-based approaches
- Dependency Parsing Applications: Information extraction and question answering
Module 7. Semantic Role Labeling: Introduction to SRL : Definition and importance
- Introduction to SRL: Definition and importance
- SRL Techniques: Rule-based and machine learning approaches
- SRL Applications: Information extraction and question answering
Module 8. Coreference Resolution: Introduction to : Definition and importance
- Introduction to Coreference Resolution: Definition and importance
- Coreference Resolution Techniques: Rule-based and machine learning approaches
- Coreference Resolution Applications: Information extraction and question answering
Module 9. Question Answering: Introduction to : Definition and importance
- Introduction to Question Answering: Definition and importance
- Question Answering Techniques: Rule-based and machine learning approaches
- Question Answering Applications: Virtual assistants and customer support
Module 10. Dialogue Systems: Introduction to : Definition and importance
- Introduction to Dialogue Systems: Definition and importance
- Dialogue Systems Techniques: Rule-based and machine learning approaches
- Dialogue Systems Applications: Virtual assistants and customer support
Module 11. Natural Language Generation: Introduction to NLG : Definition and importance
- Introduction to NLG: Definition and importance
- NLG Techniques: Rule-based and machine learning approaches
- NLG Applications: Content generation and language translation
Module 12. Evaluation Metrics: NLG : BLEU, ROUGE, and METEOR
- Introduction to Evaluation Metrics: Definition and importance
- Evaluation Metrics for NLU: Accuracy, precision, recall, and F1-score
- Evaluation Metrics for NLG: BLEU, ROUGE, and METEOR
Module 13. Advanced Topics in NLU: Transfer Learning : Introduction and applications
- Attention Mechanisms: Introduction and applications
- Transfer Learning: Introduction and applications
- Adversarial Training: Introduction and applications
Module 14: Practical Tools and Techniques for Self-Assessment
- NLTK and spaCy: Introduction and applications
- TensorFlow and PyTorch: Introduction and applications
- Self-Assessment Techniques: Evaluation metrics and visualization tools
Certificate
Upon completion of the course, participants will receive a certificate issued by The Art of Service.Target Audience
This course is designed for anyone interested in Natural Language Understanding, including:- Students and researchers in NLP and AI
- Developers and engineers working on NLP projects
- Data scientists and analysts working with text data
- Business professionals interested in NLP applications