Deep Learning for NLP: GitHub Bug Prediction Analysis

Natural Language Processing    |    Intermediate
  • 13 Videos | 1h 55m 32s
  • Includes Assessment
  • Earns a Badge
Get down to solving real-world GitHub bug prediction problems in this case study course. Examine the process of data and library loading and perform basic exploratory data analysis (EDA) including word count, label, punctuation, and stop word analysis. Explore how to clean and preprocess data in order to use vectorization and embeddings and use counter vector and term frequency-inverse document frequency (TFIDF) vectorization methods with visualizations. Finally, assess different classifiers like logistic regression, random forest, or AdaBoost. Upon completing this course, you will understand how to solve industry-level problems using deep learning methodology in the TensorFlow ecosystem.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    explain GitHub bug data and problem statement
    perform library loading, data loading, and basic overview of columns
    read a CSV file in Google Colab
    demonstrate Exploratory Data Analysis (EDA) - word count analysis and label analysis
    demonstrate EDA - punctuation analysis, stop word analysis, and word cloud
    clean and preprocess data using advanced techniques
  • clean data using functions
    use counter vector and term frequency-inverse document frequency (TFIDF) vectorization methods with visualizations
    perform advanced embeddings like Word2Vec and apply AdaBoost classifier
    work with deep learning models using embeddings
    compare and contrast logistic regression, random forest, AdaBoost, and long short-term memory (LSTM) classifiers
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 41s
    UP NEXT
  • Playable
    2. 
    Case Study: Introduction to GitHub Bug Prediction
    2m 35s
  • Locked
    3. 
    Case Study: Loading Data & Libraries
    5m
  • Locked
    4. 
    Case Study: Understanding the Data
    7m 11s
  • Locked
    5. 
    Case Study: Basic Exploratory Data Analysis
    14m 50s
  • Locked
    6. 
    Case Study: Punctuation & Stop Word Analysis
    15m 1s
  • Locked
    7. 
    Case study: Advanced Data Preprocessing
    15m 37s
  • Locked
    8. 
    Case Study: Data Cleaning
    7m 45s
  • Locked
    9. 
    Case Study: Exploring Vectorization
    14m 53s
  • Locked
    10. 
    Case Study: Exploring Embeddings
    15m 30s
  • Locked
    11. 
    Case Study: Applying Deep Learning Modeling
    12m 5s
  • Locked
    12. 
    Case Study: Performing Model Comparison
    2m 14s
  • Locked
    13. 
    Course Summary
    1m 11s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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