Explore natural language processing (NLP) concepts, review advanced data cleaning and vectorization techniques, and learn how to build machine learning classifiers.
NLP with Python for Machine Learning Essential Training
Overview
Syllabus
Introduction
- Welcome
- What you should know
- What tools do you need?
- Using the exercise files
- 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
- Introducing stemming
- Using stemming
- Introducing lemmatizing
- Using lemmatizing
- Introducing vectorizing
- Count vectorization
- N-gram vectorizing
- Inverse document frequency weighting
- Introducing feature engineering
- Feature creation
- Feature evaluation
- Identifying features for transformation
- Box-Cox power transformation
- 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
- Next steps
Taught by
Derek Jedamski
