Applied Artificial Intelligence: Natural Language Processing
Learn how Natural Language Processing is a crucial component in generative AI and understand its core problems with this online course from CloudSwyft.
Duration
3 weeks
Weekly study
6 hours
Included in an ExpertTrack
Course 3 of 4
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Gain an understanding of generative AI
This course is part of the Advanced and Applied AI on Microsoft Azure ExpertTrack, helping you develop AI and machine learning skills and prepare you for the relevant Microsoft microcredentials.
Have you ever asked Siri a question? Or told Alexa to play a song? If so, you’ve experienced the most powerful and advanced examples of Natural Language Processing (NLP) that exist today.
NLP is fast becoming a part of our day-to-day lives and is a crucial component of AI. In this course, you’ll get an understanding of the core problems in NLP and learn how to solve critical NLP tasks.
Discover Natural Language Processing (NLP)
Natural language processing (NLP) is a field of artificial intelligence (AI) machine learning and computational linguistics.
During the course, you’ll learn how NLP is one of the most important technologies of the Information Age. You’ll gain a deeper understanding of this aspect of AI by delving into its sub areas including; natural language understanding, machine translation, semantics, and syntactic passing as well as natural language emulation and dialectal systems.
Understand vision and language joint learning
You’ll also be introduced to vision and language joint learning and inference problems to learn more about the issues of NLP.
As well as this, you’ll cover multimodal intelligence tasks and learn how to apply deep learning models on image captioning and visual question answering.
Apply deep learning models to solve NLP problems
By the end of the course, you will understand that NLP, though extremely powerful, also comes with many problems.
You’ll learn how to apply deep learning models to solve problems such as machine translation and conversation as well as applying deep reinforcement learning models on natural language applications.
What topics will you cover?
- Introduction to NLP and Deep Learning provides an overview of Natural Language Processing using classic machine learning methods and cutting-edge deep learning methods.
- Neural models for machine translation and conversation gives an introduction to Statistical Machine Translation and neural models for translation and conversation.
- Deep Semantic Similarity Models (DSSM) gives an introduction to Deep Semantic Similarity Model (DSSM) and its applications.
- Natural Language Understanding gives an introduction to methods applied in Natural Language Understanding, such as continuous word representations and neural knowledge base embedding.
- Deep reinforcement learning in NLP gives an introduction to deep reinforcement learning techniques applied in NLP.
- Vision-Language Multimodal Intelligence gives an introduction to neural models applied in Image captioning and visual question answering.
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