Software Development
Understanding Natural Language Processing
Natural Language Processing: Getting Started with NLP
it_nlunlpdj_01_enus
Natural Language Processing: Linguistic Features Using NLTK & spaCy
it_nlunlpdj_02_enus
Natural Language Processing: Getting Started with NLP
Lesson Objectives
Natural Language Processing: Getting Started with NLP
- discover the key concepts covered in this course
- describe the foundation of natural language and its processing
- illustrate phonemes, morpheme, and lexemes
- describe syntactic and semantic analysis for NLP
- illustrate various fundamental tasks and components that are solved and explored using NLP
- describe heuristic approaches to solve NLP tasks
- describe machine learning approaches to solve NLP tasks
- describe deep learning approaches to solve NLP tasks
- specify challenges with NLP in real world problem solving
- illustrate various tools in NLP used across different industries
- illustrate various use cases in NLP across different industries
- summarize the key concepts covered in this course
Overview/Description
Enterprises across the world are creating large amounts of language data. There are many different kinds of data with language components including reports, word documents, operational data, emails, reviews, sops, and legal documents. This course will help you develop the skills to analyze this data and extract valuable and actionable insights. Learn about the various building blocks of natural language processing to help in understanding the different approaches used for solving NLP problems. Examine machine learning and deep learning approaches to handling NLP issues. Finally, explore common use cases that companies are approaching with NLP solutions. Upon completion of this course, you will have a strong foundation in the fundamentals of natural language processing, its building blocks, and the various approaches that can be used to architect solutions for enterprises in NLP domains.
Enterprises across the world are creating large amounts of language data. There are many different kinds of data with language components including reports, word documents, operational data, emails, reviews, sops, and legal documents. This course will help you develop the skills to analyze this data and extract valuable and actionable insights. Learn about the various building blocks of natural language processing to help in understanding the different approaches used for solving NLP problems. Examine machine learning and deep learning approaches to handling NLP issues. Finally, explore common use cases that companies are approaching with NLP solutions. Upon completion of this course, you will have a strong foundation in the fundamentals of natural language processing, its building blocks, and the various approaches that can be used to architect solutions for enterprises in NLP domains.
Target
Prerequisites: none
Natural Language Processing: Linguistic Features Using NLTK & spaCy
Lesson Objectives
Natural Language Processing: Linguistic Features Using NLTK & spaCy
- discover the key concepts covered in this course
- categorize various linguistic features available to help in language processing
- provide a basic overview of the Natural Language Toolkit (NLTK) ecosystem
- provide a basic overview of the spaCy ecosystem
- classify the difference between spaCy and NLTK
- demonstrate how to use NLTK setup, word corpora, tokenization, cleaner, stemming, lemmatization, stop words, rare words, and spell correction in NLTK
- demonstrate the use of parts of speech, n-gram, named entity recognition, dependency parsing, chunking, parsers, and other language support in NLTK
- recognize what spaCy models are and the various types of spaCy models
- install and import spaCy libraries, and extract basic NLP features such as parts of speech, morphology, and lemmatization
- demonstrate dependency parsing, named entities, and entity linking with spaCy
- work with spaCy to tokenize, merge, and split data
- demonstrate sentence segmentation and sentence similarity with spaCy
- summarize the key concepts covered in this course
Overview/Description
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.
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.
Target
Prerequisites: none
