Text Data Cleaning and Pre-processing Techniques
Master the art of cleaning and pre-processing text data! This course will teach you the essential techniques to refine text for NLP projects.
What you'll learn
Cleaning and pre-processing text data is often the first and most crucial step in NLP.
In this course, Text Data Cleaning and Pre-processing Techniques, you’ll gain the ability to transform raw text into a clean, structured format ready for analysis.
First, you’ll explore the fundamental characteristics of textual data and learn to identify common issues such as noise and missing data.
Next, you’ll discover techniques for cleaning and handling missing data, along with basic noise removal strategies.
Finally, you’ll learn how to utilize advanced text pre-processing techniques, including text normalization, tokenization, and handling special characters and emojis.
When you’re finished with this course, you’ll have the skills and knowledge of text data pre-processing needed to enhance the quality and reliability of your NLP models.
Table of contents
- Introduction to Textual Data Characteristics and Structure 3m
- Importance of Data Cleaning in Text Analysis 1m
- Ethical Considerations in Text Data Analysis 4m
- Recognizing Common Challenges in Text Data 2m
- Techniques for Handling Missing Data in Text Datasets 1m
- Basic Noise Removal Techniques 2m
- Demo: Analyzing Text Dataset 5m
- Overview of Text Normalization and Its Importance 2m
- Implementing Stemming and Lemmatization 4m
- Exploring Various Tokenization Techniques 2m
- Advanced Handling of Special Characters 3m
- Practical Application of Normalization and Tokenization 3m
- Balance between Preserving Information and Removing 3m
- Summary 1m
