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
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discover the key concepts covered in this courseexplain GitHub bug data and problem statementperform library loading, data loading, and basic overview of columnsread a CSV file in Google Colabdemonstrate Exploratory Data Analysis (EDA) - word count analysis and label analysisdemonstrate EDA - punctuation analysis, stop word analysis, and word cloudclean and preprocess data using advanced techniques
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clean data using functionsuse counter vector and term frequency-inverse document frequency (TFIDF) vectorization methods with visualizationsperform advanced embeddings like Word2Vec and apply AdaBoost classifierwork with deep learning models using embeddingscompare and contrast logistic regression, random forest, AdaBoost, and long short-term memory (LSTM) classifierssummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview1m 41sUP NEXT
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2.Case Study: Introduction to GitHub Bug Prediction2m 35s
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3.Case Study: Loading Data & Libraries5m
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4.Case Study: Understanding the Data7m 11s
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5.Case Study: Basic Exploratory Data Analysis14m 50s
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6.Case Study: Punctuation & Stop Word Analysis15m 1s
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7.Case study: Advanced Data Preprocessing15m 37s
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8.Case Study: Data Cleaning7m 45s
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9.Case Study: Exploring Vectorization14m 53s
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10.Case Study: Exploring Embeddings15m 30s
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11.Case Study: Applying Deep Learning Modeling12m 5s
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12.Case Study: Performing Model Comparison2m 14s
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13.Course Summary1m 11s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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Digital badges are yours to keep, forever.