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 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
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1m 41s
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2m 35sAfter completing this video, you will be able to explain GitHub bug data and problem statement. FREE ACCESS
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3. Case Study: Loading Data & Libraries5mFind out how to load libraries and data, and get a basic overview of columns. FREE ACCESS
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4. Case Study: Understanding the Data7m 11sIn 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 Analysis14m 50sLearn how to apply Exploratory Data Analysis (EDA) - including word count analysis and label analysis. FREE ACCESS
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6. Case Study: Punctuation & Stop Word Analysis15m 1sIn this video, find out how to apply EDA: punctuation analysis, stop word analysis, and word cloud. FREE ACCESS
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7. Case study: Advanced Data Preprocessing15m 37sIn this video, you will clean and preprocess data using advanced techniques. FREE ACCESS
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8. Case Study: Data Cleaning7m 45sIn this video, learn how to clean data by using functions. FREE ACCESS
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9. Case Study: Exploring Vectorization14m 53sIn this video, you will use the counter vector and term frequency-inverse document frequency (TFIDF) vectorization methods with visualizations. FREE ACCESS
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10. Case Study: Exploring Embeddings15m 30sFind out how to perform advanced embeddings like Word2Vec and apply the AdaBoost classifier. FREE ACCESS
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11. Case Study: Applying Deep Learning Modeling12m 5sIn this video, you will work with deep learning models that use embeddings. FREE ACCESS
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12. Case Study: Performing Model Comparison2m 14sDuring 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 Summary1m 11s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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