- Acquiring text from various sources
- Cleansing and transforming text data
- Preparing TF-IDF matrices for machine learning
- Building n-grams databases for text predictions
- Best practices for scalability and storing text
Skill Level Intermediate
- Let's say you have a massive amount of text that you want to analyze. That's a fairly likely scenario, considering more and more text is being generated today. It takes the form of messages, emails, blogs and comments on social media. And in hand, the need to understand, analyze and act on this data is also growing. As such, text processing is a key skill for any data professional. What exactly is processing? I'm referring to cleansing, tokenization, aggregation and n-grams extraction. Don't know what all these terms mean? No problem. That's why I'm here. Generally speaking, processing is cleaning up raw data to make it available for analysis. My name is Kumaran Ponnambalam. In this course, I will show you the tools and techniques available for text processing in R. We will use the tm, or text mining library, to build use cases in RStudio. You need prior familiarity with R programming. That being said, let's explore how to process text with R.
Processing Text with Python Essential Trainingwith Kumaran Ponnambalam33m 31s Intermediate
1. Introduction to Text Mining
2. Corpus in R
3. Text Cleansing and Extraction
6. Best Practices
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