IIRF Online > Development > Data Science > Natural Language Processing > Applied Artificial Intelligence: Natural Language Processing
Applied Artificial Intelligence: Natural Language Processing by Futurelearn
Learn how Natural Language Processing is a crucial component in generative AI and understand its core problems.
Course Highlights
- Apply deep learning models to solve machine translation and conversation problems.
- Apply deep structured semantic models on information retrieval and natural language applications.
- Apply deep reinforcement learning models on natural language applications.
- Apply deep learning models on image captioning and visual question answering
Skills you will learn!
Curriculum
6 Topics
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.
Applied Artificial Intelligence: Natural Language Processing
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