What tools do you need? - Python Tutorial

Contents

• Introduction Introduction

  • Welcome

    1m 6s
  • What you should know

    1m 6s
  • What tools do you need?

    43s
  • Using the exercise files

    1m 9s
• 1. NLP Basics 1. NLP Basics

  • What are NLP and NLTK?

    4m 7s
  • NLTK setup and overview

    6m 15s
  • Reading in text data

    11m 41s
  • Exploring the dataset

    6m 56s
  • What are regular expressions?

    4m 8s
  • Learning how to use regular expressions

    8m 44s
  • Regular expression replacements

    6m 3s
  • Machine learning pipeline

    4m 45s
  • Implementation: Removing punctuation

    9m 10s
  • Implementation: Tokenization

    3m 37s
  • Implementation: Removing stop words

    4m 2s
• 2. Supplemental Data Cleaning 2. Supplemental Data Cleaning

  • Introducing stemming

    3m 51s
  • Using stemming

    6m 36s
  • Introducing lemmatizing

    2m 13s
  • Using lemmatizing

    8m 15s
• 3. Vectorizing Raw Data 3. Vectorizing Raw Data

  • Introducing vectorizing

    6m 51s
  • Count vectorization

    10m 47s
  • N-gram vectorizing

    10m 53s
  • Inverse document frequency weighting

    8m 43s
• 4. Feature Engineering 4. Feature Engineering

  • Introducing feature engineering

    3m 27s
  • Feature creation

    6m 33s
  • Feature evaluation

    6m 16s
  • Identifying features for transformation

    4m 2s
  • Box-Cox power transformation

    6m 59s
• 5. Building Machine Learning Classifiers 5. Building Machine Learning Classifiers

  • What is machine learning?

    4m 2s
  • Cross-validation and evaluation metrics

    7m 48s
  • Introducing random forest

    3m 4s
  • Building a random forest model

    8m 11s
  • Random forest with holdout test set

    12m 2s
  • Random forest model with grid search

    8m 48s
  • Evaluate random forest model performance

    8m 44s
  • Introducing gradient boosting

    4m 13s
  • Gradient-boosting grid search

    9m 44s
  • Evaluate gradient-boosting model performance

    9m 32s
  • Model selection: Data prep

    8m 25s
  • Model selection: Results

    9m 52s
• Conclusion Conclusion

  • Next steps

    1m 10s