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LinkedIn Learning

Advanced NLP with Python for Machine Learning

via LinkedIn Learning

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Overview

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Build upon your foundational knowledge of natural language processing (NLP) by exploring more complex topics such as word2vec, doc2vec, and recurrent neural networks.

Syllabus

Introduction
  • Leveraging the power of messy text data
  • What you should know
  • What tools you need
  • Using the exercise files
1. Review NLP Basics
  • What is NLP?
  • NLTK setup
  • Reading text data into Python
  • Cleaning text data
  • Vectorize text using TF-IDF
  • Building a model on top of vectorized text
2. word2vec
  • What is word2vec?
  • What makes word2vec powerful?
  • How to implement word2vec
  • How to prep word vectors for modeling
3. doc2vec
  • What is doc2vec?
  • What makes doc2vec powerful?
  • How to implement doc2vec
  • How to prep document vectors for modeling
4. Recurrent Neural Networks
  • What is a neural network?
  • What is a recurrent neural network?
  • What makes RNNs so powerful for NLP problems?
  • Preparing data for an RNN
  • How to implement a basic RNN
5. Compare Advance NLP Techniques on an ML Problem
  • Prep the data for modeling
  • Build a model on TF-IDF vectors
  • Build a model on word2vec embeddings
  • Build a model on doc2vec embeddings
  • Build an RNN model
  • Compare all methods using key performance metrics
  • Key takeaways for advanced NLP modeling techniques
Conclusion
  • How to continue advancing your skills

Taught by

Derek Jedamski

Reviews

4.8 rating at LinkedIn Learning based on 567 ratings

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