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LinkedIn Learning

NLP with Python for Machine Learning Essential Training

via LinkedIn Learning

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Overview

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

Syllabus

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

Taught by

Derek Jedamski

Reviews

4.8 rating at LinkedIn Learning based on 1406 ratings

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