Natural Language Processing: Linguistic Features Using NLTK & spaCy
Natural Language Processing
| Intermediate
- 13 Videos | 1h 10m 44s
- Includes Assessment
- Earns a Badge
Without fundamental building blocks and industry-accepted tools, it is difficult to achieve state-of-art analysis in NLP. In this course, you will learn about linguistic features such as word corpora, tokenization, stemming, lemmatization, and stop words and understand their value in natural language processing. Begin by exploring NLTK and spaCy, two of the most widely used NLP tools, and understand what they can help you achieve. Learn to recognize the difference between these tools and understand the pros and cons of each. Discover how to implement concepts like part of speech tagging, named entity recognition, dependency parsing, n-grams, spell correction, segmenting sentences, and finding similar sentences. Upon completion of this course, you will be able to build basic NLP applications on any raw language data and explore the NLP features that can help businesses take actionable steps with this data.
WHAT YOU WILL LEARN
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discover the key concepts covered in this coursecategorize various linguistic features available to help in language processingprovide a basic overview of the Natural Language Toolkit (NLTK) ecosystemprovide a basic overview of the spaCy ecosystemclassify the difference between spaCy and NLTKdemonstrate how to use NLTK setup, word corpora, tokenization, cleaner, stemming, lemmatization, stop words, rare words, and spell correction in NLTKdemonstrate the use of parts of speech, n-gram, named entity recognition, dependency parsing, chunking, parsers, and other language support in NLTK
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recognize what spaCy models are and the various types of spaCy modelsinstall and import spaCy libraries, and extract basic NLP features such as parts of speech, morphology, and lemmatizationdemonstrate dependency parsing, named entities, and entity linking with spaCywork with spaCy to tokenize, merge, and split datademonstrate sentence segmentation and sentence similarity with spaCysummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview44sUP NEXT
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2.Linguistic Features in Language Processing2m 42s
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3.Introduction to Natural Language Toolkit (NLTK)3m 48s
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4.Introduction to spaCy3m 43s
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5.spaCy verses NLTK1m 44s
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6.Using Linguistic Features in NLTK - Part 112m 11s
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7.Using Linguistic Features in NLTK - Part 27m 6s
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8.Types of spaCy Models3m 5s
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9.Using Linguistic Features in spaCy - Part 110m 53s
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10.Using Linguistic Features in spaCy - Part 212m 45s
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11.Using Linguistic Features in spaCy - Part 37m 8s
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12.Using Linguistic Features in spaCy - Part 44m 11s
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13.Course Summary44s
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