Text Classification for Categorizing Data Records
This course will teach you the essential techniques to categorize data records efficiently using cutting-edge NLP techniques.
What you'll learn
Unlock the secrets of text classification. In this course, Text Classification for Categorizing Data Records, you’ll gain the ability to categorize text data efficiently. First, you’ll explore text preprocessing and vectorization, laying the foundation for effective classification. Next, you’ll discover various classification models, from Naive Bayes to neural networks, and understand their unique strengths. Finally, you’ll learn how to implement multi-class and multi-label classification, equipping you to handle complex categorization tasks. When you’re finished with this course, you’ll have the skills and knowledge of text classification for practical data categorization needed to enhance your text analysis and machine learning skills.
Table of contents
- Introduction to Text Classification 2m
- Components of Text Classification Systems 6m
- Binary vs. Multi-class Classification 4m
- Applications in Data Management and Information Retrieval 4m
- Feature Selection and Its Importance 4m
- Ethical Considerations in Text Classification 2m
- Text Preprocessing and Vectorization Techniques 4m
- Demo: Preprocessing the Profiles Dataset 11m
- Exploration of Classification Models Algorithms 6m
- Model Evaluation Tuning Strategies and Best Practices 4m
- Multi-class, Multi-label, and Their Challenges 2m
- Analysis of Text Classification in Different Domains 2m
- Sentiment Analysis and Spam Detection 4m
- Content Recommendation and Information Retrieval 3m
- Demo: Classifying Professional Profiles into Multiple Categories 8m
- Summary 1m
