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.
This course is designed to empower you with essential skills for effectively handling text data in the context of natural langua...Show More
WHAT YOU'LL LEARN
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
An understanding of the significance of text preprocessing in natural language processing and its impact on text analysis tasks
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Content
1.
About This Course
1 Lessons
Get familiar with the importance and techniques of text preprocessing for effective NLP.
2.
Introduction To Text Preprocessing
4 Lessons
Look at text preprocessing techniques, data types, and processing stages for better analysis.
3.
Regular Expressions
6 Lessons
Go hands-on with regular expressions to identify patterns, preprocess, and extract text data.
4.
Irrelevant Text Data
7 Lessons
Break down the steps to effectively manage, clean, and preprocess irrelevant text data.
5.
Basic Text Preprocessing Techniques
6 Lessons
Dig deeper into text preprocessing techniques like lowercasing, punctuation removal, and handling special characters.
6.
Indexing
6 Lessons
Focus on efficient text processing through various indexing methods and implementing code challenges.
7.
Text Transformation
6 Lessons
Piece together the parts of text tokenization, normalization, stemming, lemmatization, and transformation challenges.
8.
Text Representation
6 Lessons
Learn how to use text representation techniques like BoW, TF-IDF, and word embeddings.
9.
Text Feature Engineering
6 Lessons
Unpack the core of text feature construction, scaling, date handling, and coding challenges.
10.
Advanced Text Preprocessing
6 Lessons
Go hands-on with part-of-speech tagging, named entity recognition, and text classification.
11.
N-grams
5 Lessons
Grasp the fundamentals of N-grams, their applications in text classification, and practical code challenges.
12.
Conclusion
1 Lessons
Consolidate your skills in essential text preprocessing techniques for advanced NLP tasks.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Course Author:
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