- 15 Sections
- 89 Lessons
- 40 Hours
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- PERSIAPAN2
- 1. GAINING EARLY INSIGHTS FROM TEXTUAL DATA8
- 3.11.1. Exploratory Data Analysis
- 3.21.2. Introducing the Dataset
- 3.31.3. Blueprint: Getting an Overview of the Data with Pandas
- 3.41.4. Blueprint: Building a Simple Text Preprocessing Pipeline
- 3.51.5. Blueprints for Word Frequency Analysis
- 3.61.6. Blueprint: Finding a Keyword-in-Context
- 3.71.7. Blueprint: Analyzing N-Grams
- 3.81.8. Blueprint: Comparing Frequencies Across Time Intervals and Categories
- 2. EXTRACTING TEXTUAL INSIGHTS WITH APIS3
- 3. SCRAPING WEBSITES AND EXTRACTING DATA17
- 5.13.1. Scraping and Data Extraction
- 5.23.2. Introducing the Reuters News Archive
- 5.33.3. URL Generation
- 5.43.4. Blueprint: Downloading and Interpreting robots.txt
- 5.53.5. Blueprint: Finding URLs from sitemap.xml
- 5.63.6. Blueprint: Finding URLs from RSS
- 5.73.7. Downloading Data
- 5.83.8. Blueprint: Downloading HTML Pages with Python
- 5.93.9. Blueprint: Downloading HTML Pages with wget
- 5.103.10. Extracting Semistructured Data
- 5.113.11. Blueprint: Extracting Data with Regular Expressions
- 5.123.12. Blueprint: Using an HTML Parser for Extraction
- 5.133.13. Blueprint: Spidering
- 5.143.14. Density-Based Text Extraction
- 5.153.15. All-in-One Approach
- 5.163.16. Blueprint: Scraping the Reuters Archive with Scrapy
- 5.173.17. Possible Problems with Scraping
- 4. PREPARING TEXTUAL DATA FOR STATISTICS AND MACHINE LEARNING7
- 5. FEATURE ENGINEERING AND SYNTACTIC SIMILARITY5
- 6. TEXT CLASSIFICATION ALGORITHMS6
- 8.16.1. Introducing the Java Development Tools Bug Dataset
- 8.26.2. Blueprint: Building a Text Classification System
- 8.36.3. Final Blueprint for Text Classification
- 8.46.4. Blueprint: Using Cross-Validation to Estimate Realistic Accuracy Metrics
- 8.56.5. Blueprint: Performing Hyperparameter Tuning with Grid Search
- 8.66.6. Blueprint Recap and Conclusion
- 7. HOW TO EXPLAIN A TEXT CLASSIFIER5
- 9.17.1. Blueprint: Determining Classification Confidence Using Prediction Probability
- 9.27.2. Blueprint: Measuring Feature Importance of Predictive Models
- 9.37.3. Blueprint: Using LIME to Explain the Classification Results
- 9.47.4. Blueprint: Using ELI5 to Explain the Classification Results
- 9.57.5. Blueprint: Using Anchor to Explain the Classification Results
- 8. UNSUPERVISED METHODS: TOPIC MODELING AND CLUSTERING9
- 10.18.1. Our Dataset: UN General Debates
- 10.28.2. Nonnegative Matrix Factorization (NMF)
- 10.38.3. Latent Semantic Analysis/Indexing
- 10.48.4. Latent Dirichlet Allocation
- 10.58.5. Blueprint: Using Word Clouds to Display and Compare Topic Models
- 10.68.6. Blueprint: Calculating Topic Distribution of Documents and Time Evolution
- 10.78.7. Using Gensim for Topic Modeling
- 10.88.8. Blueprint: Using Clustering to Uncover the Structure of Text Data
- 10.98.9. Further Ideas
- 9. TEXT SUMMARIZATION5
- 10. EXPLORING SEMANTIC RELATIONSHIPS WITH WORD EMBEDDINGS4
- 11. PERFORMING SENTIMENT ANALYSIS ON TEXT DATA7
- 13.111.1. Sentiment Analysis
- 13.211.2. Introducing the Amazon Customer Reviews Dataset
- 13.311.3. Blueprint: Performing Sentiment Analysis Using Lexicon-Based Approaches
- 13.411.4. Supervised Learning Approaches
- 13.511.5. Blueprint: Vectorizing Text Data and Applying a Supervised Machine Learning Algorithm
- 13.611.6. Pretrained Language Models Using Deep Learning
- 13.711.7. Blueprint: Using the Transfer Learning Technique and a Pretrained Language Model
- 12. BUILDING A KNOWLEDGE GRAPH6
- 13. USING TEXT ANALYTICS IN PRODUCTION5
- 16.113.1. Blueprint: Using Conda to Create Reproducible Python Environments
- 16.213.2. Blueprint: Using Containers to Create Reproducible Environments
- 16.313.3. Blueprint: Creating a REST API for Your Text Analytics Model
- 16.413.4. Blueprint: Deploying and Scaling Your API Using a Cloud Provider
- 16.513.5. Blueprint: Automatically Versioning and Deploying Builds
- PENUTUPAN2
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