NLP with Python for Machine Learning Essential Training

Explore natural language processing (NLP) concepts, review advanced data cleaning and vectorization techniques, and learn how to build machine learning classifiers.

Introduction

• Welcome
• What you should know
• What tools do you need?
• Using the exercise files

1. NLP Basics

• What are NLP and NLTK?
• NLTK setup and overview
• Reading in text data
• Exploring the dataset
• What are regular expressions?
• Learning how to use regular expressions
• Regular expression replacements
• Machine learning pipeline
• Implementation: Removing punctuation
• Implementation: Tokenization
• Implementation: Removing stop words

2. Supplemental Data Cleaning

• Introducing stemming
• Using stemming
• Introducing lemmatizing
• Using lemmatizing

3. Vectorizing Raw Data

• Introducing vectorizing
• Count vectorization
• N-gram vectorizing
• Inverse document frequency weighting

4. Feature Engineering

• Introducing feature engineering
• Feature creation
• Feature evaluation
• Identifying features for transformation
• Box-Cox power transformation

5. Building Machine Learning Classifiers

• What is machine learning?
• Cross-validation and evaluation metrics
• Introducing random forest
• Building a random forest model
• Random forest with holdout test set
• Random forest model with grid search
• Evaluate random forest model performance
• Introducing gradient boosting
• Gradient-boosting grid search
• Evaluate gradient-boosting model performance
• Model selection: Data prep
• Model selection: Results

Conclusion

• Next steps