NLP for ML with Python: NLP Using Python & NLTK
Machine Learning
| Intermediate
- 13 Videos | 1h 1m 58s
- Includes Assessment
- Earns a Badge
This course explores how natural language processing (NLP) is used for machine learning, and examines the benefits and challenges of NLP when creating an application that can essentially understand human language. In its 13 videos, learners will be shown the essential components of NLP, including parsers, corpus, and corpus linguistic, as well as how to implement regular expressions. This course goes on to examine tokenization, a way to separate a piece of text into smaller units, and then illustrates different tokenization use cases with NLTK (Natural Language Toolkit). You will learn to use stop words using libraries and the NLTK. This course demonstrates how to implement regular expressions in Python to build NLP-powered applications. Learners will examine the list of Python NLP libraries along with their essential capabilities, including NLTK, Gensim, CoreNLP, spaCy and PyNLPl. You will learn to set up and configure an NLTK environment to illustrate how to process raw text. Finally, this course demonstrates the use of filtering stopwords in a tokenized sentence using NLTK.
WHAT YOU WILL LEARN
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discover the key concepts covered in this coursedefine NLP, it uses, and the benefits and challenges associated with itrecall essential NLP terms and the steps involved in natural language processingdescribe the rule-based and probabilistic parsing approaches and the different types of parsers that are used in NLPdefine corpus and corpus linguistic and describe the benefits associated with corpus linguisticimplement regular expressions in Pythonlist prominent Python NLP libraries and their capabilities
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set up and configure the NLTK environment to illustrate how to process raw textsrecognize the major components of NLPdefine tokenization and illustrate different tokenization use cases with NLTKdemonstrate various tokenization use cases with NLTKfilter stop words in a tokenized sentence using NLTKlist NLP terminologies, recall Python NLP libraries, and filter stop words in a tokenized sentence using NLTK
IN THIS COURSE
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1.Course Overview1m 27sUP NEXT
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2.Uses and Challenges of NLP6m 8s
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3.Terminologies and Steps of NLP10m 38s
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4.Parsing Approach and Parser Types6m 11s
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5.Corpus and Corpus Linguistic6m 51s
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6.Regular Expressions in Python4m 45s
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7.NLP Libraries3m 44s
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8.NLTK Setup4m 6s
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9.Components of NLP3m 42s
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10.Tokenization4m 26s
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11.Tokenization with NLTK3m 56s
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12.Stop Words with NLTK3m 17s
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13.Exercise: NLP Terminologies and Stopworks2m 49s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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