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Applied Artificial Intelligence: Natural Language Processing
Cloudswyft via FutureLearn
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Overview
This course focuses on applying AI techniques to natural language data. Learners explore NLP fundamentals including tokenization, sentiment analysis, named entity recognition, and language modeling. Using tools such as Python, spa Cy, and Hugging Face Transformers, participants will build models to analyze, summarize, and generate human language. The course emphasizes applied learning, guiding students through the creation of chatbots, text classifiers, and content summarizers. By the end, students will be well-equipped to apply NLP in business, healthcare, finance, or any...
Syllabus
- Course Introduction
- About this Course
- Introduction to NLP
- NLP and Text Processing
- Neural Models for Machine Translation and Conversation Generation
- CloudSwyft Hands-On Lab 1
- Wrapping up the week
- Deep Semantic Similarity Model (DSSM)
- Deep Semantic Similarity Model and its Applications
- Deep Semantic Similarity Model for Information Retrieval
- Deep Semantic Similarity Model for Entity Ranking
- CloudSwyft Hands-On Lab 2
- Deep Reinforcement Learning
- Vision-Language Multimodal Intelligence
- Wrapping up the Week
- Natural Language Understanding
- Natural Language Understanding
- Continuous Word Representation
- Neural Knowledge Base Embedding
- Knowledge-Based Question Answering
- CloudSwyft Hands-On Lab 3
- Wrapping up the Week & Course Completion
Instructor
Futurelearn
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