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Text Preprocessing with Python
Gain insights into text preprocessing with Python. Explore text cleaning, normalization, and advanced techniques like BoW and TF-IDF. Discover skills to handle unstructured data effectively for NLP.
4.3
60 Lessons
2 Projects
16h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- An understanding of the significance of text preprocessing in natural language processing and its impact on text analysis tasks
- Hands-on experience applying various text-cleaning techniques, including the use of regular expressions and lowercase and uppercase transformations
- The ability to evaluate the effectiveness of different text normalization techniques
- Familiarity with designing and implementing different text representation models, such as bag-of-words and TF-IDF
- Hands-on experience applying various text preprocessing techniques and models in a real-world application
Learning Roadmap
2.
Introduction To Text Preprocessing
Introduction To Text Preprocessing
Look at text preprocessing techniques, data types, and processing stages for better analysis.
3.
Regular Expressions
Regular Expressions
6 Lessons
6 Lessons
Go hands-on with regular expressions to identify patterns, preprocess, and extract text data.
4.
Irrelevant Text Data
Irrelevant Text Data
7 Lessons
7 Lessons
Break down the steps to effectively manage, clean, and preprocess irrelevant text data.
5.
Basic Text Preprocessing Techniques
Basic Text Preprocessing Techniques
6 Lessons
6 Lessons
Dig deeper into text preprocessing techniques like lowercasing, punctuation removal, and handling special characters.
6.
Indexing
Indexing
6 Lessons
6 Lessons
Focus on efficient text processing through various indexing methods and implementing code challenges.
7.
Text Transformation
Text Transformation
6 Lessons
6 Lessons
Piece together the parts of text tokenization, normalization, stemming, lemmatization, and transformation challenges.
8.
Text Representation
Text Representation
6 Lessons
6 Lessons
Learn how to use text representation techniques like BoW, TF-IDF, and word embeddings.
9.
Text Feature Engineering
Text Feature Engineering
6 Lessons
6 Lessons
Unpack the core of text feature construction, scaling, date handling, and coding challenges.
10.
Advanced Text Preprocessing
Advanced Text Preprocessing
6 Lessons
6 Lessons
Go hands-on with part-of-speech tagging, named entity recognition, and text classification.
11.
N-grams
N-grams
5 Lessons
5 Lessons
Grasp the fundamentals of N-grams, their applications in text classification, and practical code challenges.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
This course is designed to empower you with essential skills for effectively handling text data in the context of natural language processing (NLP). You’ll embark on a transformative journey that will equip you with a solid foundation in text manipulation, enabling you to tackle the challenges of unstructured data.
The course discusses both fundamental and advanced text preprocessing techniques. You’ll learn how to clean text and remove noise, irrelevant characters, and inconsistencies in text data. Once the data is ready for analysis, you’ll learn text normalization techniques such as stemming, lemmatization, and casing. In addition to mastering preprocessing fundamentals, you’ll also learn techniques such as bag-of-words (BoW) and term frequency-inverse document frequency (TF-IDF).
By the end of the course, you’ll be able to position yourself for success in a data-centric world where the ability to extract meaning from unstructured textual information is a prized skill.
ABOUT THE AUTHOR
Valentine Mwangi
Highly Experienced Educator and Data Scientist Empowering Thousands of Students Worldwide with Cutting-Edge Data Science Education and Interactive Workshops.
Trusted by 2.9 million developers working at companies
A
Anthony Walker
@_webarchitect_
E
Evan Dunbar
ML Engineer
S
Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
V
Vinay Krishnaiah
Software Developer
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