Introduction to the Course

What is NLP?

Humans communicate with each other using words and sentences. This way of communication is known as natural language. While humans can easily understand natural language, for computers, it's not that simple because they can only communicate in binary.

Natural language processing (NLP) is the branch of Computer Science and Artificial Intelligence concerned with granting computers the ability to parse and understand text and speech the way humans do.

NLP uses the rules of computational linguistics combined with machine learning and deep learning models. These technologies work in tandem with each other to enable computers to process human language in computer-readable formats and to understand its meaning.

Course overview

spaCy is an industrial-grade, efficient NLP Python library. It offers various pre-trained models and ready-to-use features. This course provides learners with end-to-end coverage of spaCy features and real-world applications.

The course equips learners with practical illustrations for pattern matching and helps you advance into the world of semantics with word vectors. Statistical information extraction methods are also explained in detail. Later, we'll cover an interactive business case study that shows learners how to combine spaCy features to create a real-world NLP pipeline. We'll implement ML models such as sentiment analysis, intent recognition, and context resolution. The course further focuses on classification with popular frameworks such as TensorFlow's Keras API and spaCy. We'll cover popular topics, including intent classification and sentiment analysis, and use them on popular datasets and interpret the classification results.

By the end of this course, we'll be able to confidently use spaCy, including its linguistic features, word vectors, and classifiers, to create our own NLP apps.

Who is the course for?

This course is for data scientists and machine learners who want to excel in NLP as well as NLP developers who want to master spaCy and build applications with it. Language and speech professionals who want to get hands-on experience with Python and spaCy and software developers who want to prototype applications with spaCy quickly will also find this course helpful.

Prerequisites for this course

• Basic knowledge of Python

• A beginner-level understanding of linguistic terminologies, such as parsing, POS tags, and semantic similarity, will be useful. But it is not a necessity.

• A rudimentary understanding of NLP will be helpful, but it is by no means necessary.