Natural language processing (NLP) is a set of tools and techniques that enables us to unlock the power of analyzing text. In this course, Implement Natural Language Processing for Word Embedding, you’ll learn how to use word embeddings to use neural networks for NLP. First, you’ll explore what word embeddings are and the most basic embedding: one hot encoding. Next, you’ll discover how to use word embeddings to do sentiment analysis. Finally, you’ll learn how to fine-tune existing word embeddings to improve your models as well as debase our embeddings for fairness. When you’re finished with this course, you’ll have the skills and knowledge of natural language processing needed to leverage word embeddings to create amazing NLP solutions with deep learning.
Author Name: Axel Sirota
Author Description:
Axel Sirota is a Microsoft Certified Trainer with a deep interest in Deep Learning and Machine Learning Operations. He has a Masters degree in Mathematics and after researching in Probability, Statistics and Machine Learning optimisation, he works as an AI and Cloud Consultant as well as being an Author and Instructor at Pluralsight, Develop Intelligence, and O’Reilly Media.

Table of Contents

• Course Overview
  1min
• Why Process Text?
  10mins
• Training Word Representations
  51mins
• Fine-tuning Word Representations
  30mins