This 28-part course consists tutorials, quizzes, hands-on assignments and real-world projects to learn natural language processing.
Natural language processing comprises of a set of computational techniques to understand natural languages such as English, Spanish, Chinese, etc.
The primary objectives of this course are as follows:
- Understand and implement NLP techniques for sentiment classification, information retrieval (search engines) and topic modeling.
- Understand and implement NLP techniques for uncovering text syntax and structure. That is, predicting part-of-speech tags, parse tree structure, named-entities like people and places, etc.
- Understand and implement NLP techniques for some non-traditional topics such as language identification, spelling correction, and creating word clouds.
- Bonus: Understand and implement deep learning methods for NLP (also called Deep NLP), and apply them to text generation and language translation. These methods represent the state-of-the-art for advanced tasks such as language translation, question answering, speech recognition and music composition and power systems like the Google Assistant and Amazon Alexa.
Prerequisites: Python and Linear Algebra, Statistics and Probability (Review).
Related course: Machine Learning.
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