Advanced NLP with Python for Machine Learning (2020)

2h 15mAdvanced2020-08-10

Authors

Derek Jedamski

Skilled Data Scientist specializing in machine learning

Course details

An incredible amount of unstructured text data is generated every day by social media, web pages, and a variety of other sources. But without the ability to tame and harness that data, you'll be unable to glean any value from it. In this course, learn how to translate messy text data into powerful insights using Python. Instructor Derek Jedamski begins with a quick review of foundational NLP concepts, including how to clean text data and build a model on top of vectorized text. He then jumps into more complex topics such as word2vec, doc2vec, and recurrent neural networks. To wrap up the course, he lends these concepts a real-world context by applying them to a machine learning problem.

Skills covered

Natural Language Processing (NLP)Machine LearningAdvancedPythonArtificial Intelligence (AI)Programming LanguagesOpen SourceSoftware Development

Concepts

0. Introduction

01 - Leveraging the power of messy text data
02 - What you should know
03 - What tools you need
04 - Using the exercise files

1. Review NLP Basics

05 - What is NLP
06 - NLTK setup
07 - Reading text data into Python
08 - Cleaning text data
09 - Vectorize text using TF-IDF
10 - Building a model on top of vectorized text

2. word2vec

11 - What is word2vec
12 - What makes word2vec powerful
13 - How to implement word2vec
14 - How to prep word vectors for modeling

3. doc2vec

15 - What is doc2vec
16 - What makes doc2vec powerful
17 - How to implement doc2vec
18 - How to prep document vectors for modeling

4. Recurrent Neural Networks

19 - What is a neural network
20 - What is a recurrent neural network
21 - What makes RNNs so powerful for NLP problems
22 - Preparing data for an RNN
23 - How to implement a basic RNN

5. Compare Advance NLP Techniques on an ML Problem

24 - Prep the data for modeling
25 - Build a model on TF-IDF vectors
26 - Build a model on word2vec embeddings
27 - Build a model on doc2vec embeddings
28 - Build an RNN model
29 - Compare all methods using key performance metrics
30 - Key takeaways for advanced NLP modeling techniques

Conclusion

31 - How to continue advancing your skills