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This is the curriculum for "Learn Natural Language Processing" by Siraj Raval on Youtube
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Learn-Natural-Language-Processing-Curriculum

This is the curriculum for "Learn Natural Language Processing" by Siraj Raval on Youtube

Course Objective

This is the Curriculum for this video on Learn Natural Language Processing by Siraj Raval on Youtube. After completing this course, start your own startup, do consulting work, or find a full-time job related to NLP. Remember to believe in your ability to learn. You can learn NLP , you will learn NLP, and if you stick to it, eventually you will master it.

Find a study buddy

Join the #NLP_curriculum channel in our Slack channel to find one http://wizards.herokuapp.com

Components each week

  • Video Lectures
  • Reading Assignments
  • Project(s)

Course Length

  • 8 weeks
  • 2-3 Hours of Study per Day

Tools Used

  • Python, PyTorch, NLTK

Prerequisites

Week 1 - Language Terminology + preprocessing techniques

Description:

  • Overview of NLP (Pragmatics, Semantics, Syntax, Morphology)
  • Text preprocessing (stemmings, lemmatization, tokenization, stopword removal)

Video Lectures

Reading Assignments:

  • Ch 1-2 of Speech and Language Processing 3rd ed, slides

Project:

Week 2 - Language Models & Lexicons (pre-deep learning)

Description:

  • Lexicons
  • Pre-deep learning Statistical Language model pre-deep learning ( HMM, Topic Modeling w LDA)

Video Lectures:

Reading Assignments:

  • 4,6,7,8,9,10 from the UWash course

Extra

Project

Week 3 - Word Embeddings (Word, sentence, and document)

Video lectures:

Reading Assignments

  • Suggested readings from course

Project

  • 3 Assignments Visualize and Implement Word2Vec, Create dependency parser all in PyTorch (they are assigments from the stanford course)

Week 4-5 - Deep Sequence Modeling

Description:

  • Sequence to Sequence Models (translation, summarization, question answering)
  • Attention based models
  • Deep Semantic Similarity

Video Lectures

Reading Assignments

Project

  • 3 Assignments, create a translator and a summarizer. All seq2seq models. In pytorch.

Week 6 - Dialogue Systems

Description

  • Speech Recognition
  • Dialog Managers, NLU

Video Lectures

Reading Assignments

Project

Week 7 - Transfer Learning

Video Lectures

Reading Assignments

Project

Week 8 - Future NLP

Description

  • Visual Semantics
  • Deep Reinforcement Learning

Video Lectures

Reading assignments

Project:

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