Hands On Training

Applied Artificial Intelligence: Natural Language Processing

Future Learn

Course Features

Duration

3 weeks

Delivery Method

Online

Available on

Lifetime Access

Accessibility

Mobile, Desktop

Language

English

Subtitles

English

Level

Intermediate

Effort

6 hours per week

Teaching Type

Self Paced

Course Description

This course is part of the Advanced and Applied AI in Microsoft Azure ExpertTrack and focuses on developing AI and machine-learning skills. It aims to prepare students for Microsoft microcredentials. The course highlights the importance of Natural Language Processing (NLP) in our daily lives and its crucial role in AI. NLP is an area of AI, machine learning, and computational linguistics that deals with processing and understanding human language. The course covers various sub-areas of NLP, including natural language understanding, machine translation, semantics, syntactic parsing, dialectal systems, and natural language emulation. Additionally, students will have the opportunity to explore language and vision joint learning, multimodal intelligence tasks, and deep learning models for image captioning and visual query answering. However, NLP is not without its challenges, and the course will address these issues using deep learning models for machine translation, conversation, and deep reinforcement learning models for natural language applications. It's important to note that this course is accredited by Microsoft.

Course Overview

Alumni Network

International Faculty

Post Course Interactions

Instructor-Moderated Discussions

Skills You Will Gain

What You Will Learn

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

Target Students

This course is ideal for anyone who wants to become familiar with this emerging domain of artificial intelligence (AI), including data scientists, analytics managers, data analysts, data engineers, and data architects

Course Content

Expand all sections

Module 1: Introduction to NLP and Deep Learning provides 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 gives an introduction to Statistical Machine Translation and neural models for translation and conversation.

Module 3: Deep Semantic Similarity Models (DSSM) gives an introduction to Deep Semantic Similarity Model (DSSM) and its applications.

Module 4: Natural Language Understanding gives an introduction to methods applied in Natural Language Understanding, such as continuous word representations and neural knowledge base embedding.

Course Accreditations