Processing Text with Python Essential Training
33mIntermediate2019-06-20
Authors
Kumaran Ponnambalam
Working with data for 20+ years
Course details
In the world of big data, more and more information is consumed and analyzed in text form. Websites, social media, emails, and chats have become the key sources for data and insights. If you work with data, then understanding how to deal with unstructured text data is essential. In this course, instructor Kumaran Ponnambalam helps you build your text mining skill set, covering key techniques for extracting, cleansing, and processing text in Python. Kumaran reviews key text processing concepts like tokenization and stemming. He also looks at techniques for converting text into analytics-ready form, including n-grams and TF-IDF. Along the way, he provides examples of these techniques using Python and the NLTK library.
Learning objectives
Interpret the relationship of documents inside a corpus.
Distinguish between the different text processing capabilities that the NLTK provides.
Explain why text cleansing and extraction occur when processing text with Python.
Apply advanced text processing steps to find and create TF-IDF and the TF-IDF array.
Explain best practices when processing text with Python.
Learning objectives
Interpret the relationship of documents inside a corpus.
Distinguish between the different text processing capabilities that the NLTK provides.
Explain why text cleansing and extraction occur when processing text with Python.
Apply advanced text processing steps to find and create TF-IDF and the TF-IDF array.
Explain best practices when processing text with Python.
Skills covered
Data EngineeringPythonData AnalysisEssential TrainingProgramming LanguagesData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen SourceSoftware Development
Concepts
0. Introduction
- 01 - The need for text mining skills in data science
1. Text Mining
- 02 - Text mining today
- 03 - Document concepts
- 04 - Corpus concepts
- 05 - Introduction to the NLTK library
- 06 - Setting up the environment
2. Reading Text
- 07 - Reading raw files
- 08 - Reading files with corpus reader
- 09 - Exploring the corpus
- 10 - Analyzing the corpus
3. Text Cleansing and Extraction
- 11 - Tokenization
- 12 - Cleansing text
- 13 - Stop word removal
- 14 - Stemming
- 15 - Lemmatization
4. Advanced Text Processing
- 16 - Building n-grams
- 17 - Tagging parts of speech
- 18 - Term frequency-inverse document frequency (TF-IDF)
- 19 - Building a TF-IDF matrix
5. Best Practices
- 20 - Storing text
- 21 - Processing text data
- 22 - Scalable processing of text data
Conclusion
- 23 - Next steps
Related courses
- Python Standard Library Essential Training
- NLP with Python for Machine Learning Essential Training
- Deep Learning with Python: Optimizing Deep Learning Models
- Building with the Claude API by Anthropic
- Advanced NLP with Python for Machine Learning (2020)
- AI Projects with Python, TensorFlow, and NLTK
- Building Apps with Azure AI Language and Python
- Hands-On AI: Introduction to Retrieval-Augmented Generation (RAG)
Related learn paths
- Advance Your Skills in Natural Language Processing
- Machine Learning with Python Professional Certificate by Anaconda
- Moving from Data Scientist to Data Analyst
- Advance Your Skills in AI and Machine Learning
- Technical Program Management
- Advance Your Skills in Deep Learning and Neural Networks
- Become a Software Developer
- Develop Your NoSQL Skills