Deep Learning for NLP: GitHub Bug Prediction Analysis

Natural Language Processing    |    Intermediate
  • 13 videos | 1h 55m 32s
  • Includes Assessment
  • Earns a Badge
Rating 4.3 of 3 users Rating 4.3 of 3 users (3)
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

  • 1m 41s
  • 2m 35s
    After completing this video, you will be able to explain GitHub bug data and problem statement. FREE ACCESS
  • Locked
    3.  Case Study: Loading Data & Libraries
    5m
    Find out how to load libraries and data, and get a basic overview of columns. FREE ACCESS
  • Locked
    4.  Case Study: Understanding the Data
    7m 11s
    In this video, you will learn how to read a CSV file in Google Colab. FREE ACCESS
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    5.  Case Study: Basic Exploratory Data Analysis
    14m 50s
    Learn how to apply Exploratory Data Analysis (EDA) - including word count analysis and label analysis. FREE ACCESS
  • Locked
    6.  Case Study: Punctuation & Stop Word Analysis
    15m 1s
    In this video, find out how to apply EDA: punctuation analysis, stop word analysis, and word cloud. FREE ACCESS
  • Locked
    7.  Case study: Advanced Data Preprocessing
    15m 37s
    In this video, you will clean and preprocess data using advanced techniques. FREE ACCESS
  • Locked
    8.  Case Study: Data Cleaning
    7m 45s
    In this video, learn how to clean data by using functions. FREE ACCESS
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    9.  Case Study: Exploring Vectorization
    14m 53s
    In this video, you will use the counter vector and term frequency-inverse document frequency (TFIDF) vectorization methods with visualizations. FREE ACCESS
  • Locked
    10.  Case Study: Exploring Embeddings
    15m 30s
    Find out how to perform advanced embeddings like Word2Vec and apply the AdaBoost classifier. FREE ACCESS
  • Locked
    11.  Case Study: Applying Deep Learning Modeling
    12m 5s
    In this video, you will work with deep learning models that use embeddings. FREE ACCESS
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    12.  Case Study: Performing Model Comparison
    2m 14s
    During this video, you will learn how to compare and contrast logistic regression, random forest, AdaBoost, and long short-term memory (LSTM) classifiers. FREE ACCESS
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    13.  Course Summary
    1m 11s

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