Learning Objectives - Natural Language Processing (NLP) Workloads on Azure (15-20%)

This video tutorial covers the learning objectives of the fourth module on Natural Language Processing (NLP). It is divided into two submodules. The first submodule discusses NLP features and applications, including key phrase extraction, entity recognition, sentiment analysis, language modeling, speech recognition, synthesis, and translation. The second submodule focuses on Azure tools and services for implementing these NLP features, such as text analytics, Luis, speech service, and translation service. The module also includes examples, applications, and demos, covering 15-20% of the syllabus.

Which of the following is NOT a feature discussed in the first submodule of NLP?

• Image processing

• Entity recognition

• Key phrase extraction

• Sentiment analysis

What Azure service is used for language modeling in NLP?

• Speech Service

• Translation Service

• Text Analytics

• LUIS

Which Azure service is responsible for speech recognition and synthesis?

• LUIS

• Speech Service

• Text Analytics

• Translation Service

What percentage of the syllabus does this module cover?

• 5-10%

• 15-20%

• 35-40%

• 25-30%

What is the primary focus of the final section of the module?

• Practical applications and demos

• Historical background

• Future trends

• Theoretical concepts