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Natural Language Processing (NLP) - MooC

  • Pris: kr917,00
  • Kurskod: DEV288X-LP



This course is part of the Microsoft Professional Program in Artificial Intelligence.

Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence.

In this course, you will be given a thorough overview of Natural Language Processing and how to use classic machine learning methods. You will learn about Statistical Machine Translation as well as Deep Semantic Similarity Models (DSSM) and their applications.
We will also discuss deep reinforcement learning techniques applied in NLP and Vision-Language Multimodal Intelligence.

edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.

Övrig information


The Completion Certificate is included but if this is not required then add "MooC2018Free" in the PO Field to have this removed


  • 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.



Module 1: Introduction to NLP and Deep Learning

An overview of Natural Language Processing using classic machine learning methods and cutting-edge deep learning methods.

Module 2: Neural models for machine translation and conversation

Introduction to Statistical Machine Translation and neural models for translation and conversation

Module 3: Deep Semantic Similarity Models (DSSM)

Introduction to Deep Semantic Similarity Model (DSSM) and its applications.

Module 4: Natural Language Understanding

Introduction to methods applied in Natural Language Understanding, such as continuous word representations and neural knowledge base embedding. 

Module 5: Deep reinforcement learning in NLP

Introduction to deep reinforcement learning techniques applied in NLP

Module 6: Vision-Language Multimodal Intelligence

Introduction to neural models applied in Image captioning and visual question answering

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