Advanced NLP with Python for Machine Learning

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

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