Natural Language Processing Overview – Session 1: Natural Language Processing course will give you a detailed look at the science of applying machine learning algorithms to process large amounts of natural language data. You’ll learn the concepts of statistical machine translation and neural models, deep semantic similarity model (DSSM), neural knowledge base embedding, deep reinforcement learning technique, neural models applied in image captioning, and visual question answering using Python’s Natural Language Toolkit (NLTK).
Natural Language Processing Overview – Session 1: This course is designed to familiarize you with natural language processing using both machine and deep learning methods. It introduces you to the fundamentals of natural language processing using the most popular library, Python’s Natural Language Toolkit (NLTK). The course will teach you about statistical machine translation, deep semantic similarity models (DSSM) and their applications, deep reinforcement learning techniques applied in NLP, and vision-language multimodal intelligence.
The course is ideal for anyone who wants to become familiar with this emerging and exciting domain of AI, including:
- Data scientists
- Analytics managers
- Data analysts
- Data engineers
- Data architects
Natural Language Processing, or NLP for short, is broadly defined as the automatic manipulation of natural language, like speech and text, by software. The study of natural language processing has been around for more than 50 years and grew out of the field of linguistics with the rise of computers. In this post, you will discover what natural language processing is and why it is so important. After reading this post, you will know:
- What natural language is and how it is different from other types of data.
- What makes working with natural language so challenging.
- Where the field of NLP came from and how it is defined by modern practitioners