What problems does NLP solve?

Sentiment analysis, language translation, and named entity recognition are just a few examples of tasks classified as NLP problems. To enhance NLP solutions and applications, identifying these examples is crucial.

Does ChatGPT use NLP?

Indeed, ChatGPT implements natural language processing (NLP). In reality, NLP is a fundamental technology that enables ChatGPT to comprehend, generate, and respond to human language in a meaningful manner.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Natural Language Processing Essentials

本课程是 Mastering NLP: Tokenization, Sentiment Analysis & Neural MT 专项课程的一部分

位教师：Edureka

包含在 Coursera Plus 中

了解更多

4个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Remember key NLP concepts and terminology used in processing human language and modern AI applications.
Understand core linguistic principles like morphology, syntax, semantics, and pragmatics in NLP.
Apply Python tools and techniques to clean, preprocess, and extract features from text data effectively.
Develop and evaluate basic NLP models for tasks like text classification and named entity recognition.

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Mastering NLP: Tokenization, Sentiment Analysis & Neural MT 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有4个模块

This course introduces the fundamentals of Natural Language Processing (NLP), combining core linguistic concepts with hands-on programming techniques to help you understand how machines process human language. Whether you're new to NLP or looking to build foundational skills, this course provides a clear and practical path into one of the most exciting areas of AI and data science.

Through guided lessons and real-world examples, you'll learn how to clean, structure, and analyze text data, apply feature extraction techniques, and build basic NLP models for tasks like text classification and named entity recognition. By the end of this course, you will be able to: • Understand NLP basics and key language concepts like morphology, syntax, semantics, and pragmatics. • Apply text cleaning and preprocessing techniques using NLTK and SpaCy, including tokenization, stemming, lemmatization, and embeddings. • Analyze text features by extracting Bag of Words, TF-IDF, and Word2Vec representations. • Evaluate machine learning models built for text classification. • Create NLP solutions by implementing Named Entity Recognition and syntactic parsing. This course is ideal for beginners, data enthusiasts, and aspiring NLP practitioners who want to gain a strong foundation in natural language processing and its applications in AI. No prior experience with NLP is required. A basic understanding of Python or machine learning concepts will be helpful, but not mandatory. Join us to begin your journey into the world of Natural Language Processing and text analysis with Python!

In this module, learners will develop a foundational understanding of Natural Language Processing (NLP) and its role in interpreting and processing human language. They will explore the history of NLP, its key challenges, and real-world applications. The module also introduces essential linguistic concepts—morphology, syntax, semantics, pragmatics, and discourse—that form the basis of how machines understand and work with human language.

涵盖的内容

22个视频3篇阅读材料4个作业1个讨论话题

22个视频总计109分钟

Specialization Introduction4分钟
Course Introduction2分钟
What is NLP?5分钟
Classification and Working of NLP6分钟
History of NLP Development6分钟
Key Challenges: Ambiguity, Variation, Bias3分钟
Further Exploration of NLP Challenges3分钟
Real-World NLP Applications5分钟
Rule-Based vs. Statistical Approaches5分钟
Morphology: Words, Stems, Lemmas3分钟
Sentence Structuring3分钟
Parsing3分钟
Semantics in NLP: Understanding Meaning and Context6分钟
Pragmatics: Context and Conversational Meaning7分钟
Discourse Analysis in NLP4分钟
Steps in an NLP Workflow5分钟
Basic Text Cleaning: Stopwords, Lowercasing, Tokenization4分钟
Introduction to Word Embeddings: One-Hot Encoding5分钟
Handling Noise and Special Characters7分钟
Demonstration: Lowercasing, Stopword Removal and Tokenization6分钟
Demonstration: One-Hot Encoding5分钟
Summary of Introduction to NLP and Linguistics1分钟

3篇阅读材料总计50分钟

Welcome to Natural Language Processing Essentials10分钟
Evolution of NLP: From Rule-Based Systems to Deep Learning Approaches20分钟
Linguistics for NLP: Morphology, Syntax, and Semantics20分钟

4个作业总计48分钟

Practice Quiz: Overview of Natural Language Processing6分钟
Practice Quiz: Linguistic Basics for NLP6分钟
Practice Quiz: NLP Pipeline and Text Representation6分钟
Knowledge Check: Introduction to NLP and Linguistics30分钟

1个讨论话题总计10分钟

Introduce Yourself10分钟

This module focuses on preparing textual data for analysis by exploring techniques like tokenization, normalization, stemming, and lemmatization. Learners will also examine various feature extraction methods, including Bag-of-Words, TF-IDF, and word embeddings like Word2Vec and GloVe to represent language in machine-readable formats.

涵盖的内容

44个视频4篇阅读材料6个作业

44个视频总计199分钟

Using Regex for NLP2分钟
Types of Tokenization: Subword tokenization2分钟
Types of Tokenization: Character tokenization4分钟
Handling Punctuation and Special Characters5分钟
Normalization Techniques: Accents, Unicode, Special Characters5分钟
Demonstration: Word Tokenization3分钟
Demonstration: Subword Tokenization4分钟
Demonstration: Normalization5分钟
Rule Based Stemming3分钟
Porter Stemmer5分钟
Snowball Stemmer4分钟
Lancaster Stemmer3分钟
Lovins Stemmer, Krovetz Stemmer and Context-Aware Stemming4分钟
Introduction to Lemmatization5分钟
Applications of Lemmatization3分钟
Rule-Based, Dictionary, Hybrid and Machine Learning Based Lemmatizations3分钟
Lemmatization: Different Approaches4分钟
Rule Based Stemming and Porter Stemmer6分钟
Snowball, Lancaster and Lovins6分钟
Demonstration: Lemmatization Techniques2分钟
Demonstration: Text Blob, WordNet, and Neural Lemmatizer using Stanza2分钟
Part-of-Speech (POS) Tagging5分钟
Text Representation: Bag of Words (BoW)3分钟
Text Representation: TF-IDF5分钟
Word Embeddings: Word2Vec5分钟
Word Embeddings: GloVe3分钟
Word Embeddings: FastText4分钟
Feature extraction using Bag of Words and TF-IDF5分钟
Handling Noisy Datasets (Typos, Emojis, Abbreviations)6分钟
Processing Code-Mixed (Multilingual) Text5分钟
Text Preprocessing in Domain-Specific Contexts (e.g., Medical, Legal, Financial)4分钟
Demonstration: Handling Noisy Datasets - Typos, Emojis and Abbreviations6分钟
Demonstration: Processing Code-Mixed (Multilingual) Text4分钟
Text Classification in NLP using Common ML Models4分钟
Common ML Models: Naïve Bayes, SVM4分钟
Feature Selection for Classification5分钟
Applications and Challenges of Feature Selection3分钟
Peformance Metrics: Accuracy and Precision5分钟
Peformance Metrics: Recall and F1 Score2分钟
Supervised Learning for Text Classification6分钟
Text Classification Demo using COVID-19 Tweets Dataset5分钟
Feature Extraction, Train and Evaluate Model Performance5分钟
Comparing Models for Best Performance1分钟
Summary of Text Processing and Feature Engineering1分钟

4篇阅读材料总计75分钟

Tokenization and Normalization: Preparing Text for Language Processing20分钟
Rule-Based vs. Context-Aware Stemming and Lemmatization Techniques20分钟
Feature Extraction in NLP: From Frequency to Semantic Vectors20分钟
Text Classification with ML Models: An Introductory Overview15分钟

6个作业总计60分钟

Practice Quiz: Tokenization and Normalization6分钟
Practice Quiz: Stemming and Lemmatization6分钟
Practice Quiz: Vector Representation and Feature Extraction6分钟
Practice Quiz: Advanced Preprocessing Techniques6分钟
Practice Quiz: Basics of Text Classification6分钟
Knowledge Check: Text Processing and Feature Engineering30分钟

In this module, learners will study techniques for identifying entities and extracting structured information from text. It covers rule-based and deep learning-based NER models, dependency and constituency parsing methods, and syntactic tree construction to enable deeper text understanding.

涵盖的内容

13个视频3篇阅读材料4个作业

13个视频总计52分钟

What is NER and where It's Used?6分钟
Pretrained NER Models: SpaCy, StanfordNLP5分钟
Transformer-Based NER Models (BERT-NER, RoBERTa-Based Approaches)6分钟
Challenges in NER: Ambiguity, Overlapping Entities4分钟
Parsing Algorithms: Earley, CYK3分钟
Dependency Parsing with SpaCy & StanfordNLP2分钟
Building a Syntax Tree in Python3分钟
Demonstration: Data Preparation for Parsing2分钟
Demonstration: Constituency and Dependency Parsing5分钟
Relation Extraction Techniques4分钟
Coreference Resolution (Tracking Entities in Text)3分钟
Text Summarization: Extractive & Abstractive4分钟
Summary of Named Entity Recognition (NER) & Parsing0分钟

3篇阅读材料总计50分钟

Named Entity Recognition: Concepts, Models, and Evaluation15分钟
Constituency and Dependency Parsing: Understanding Sentence Structure20分钟
From Entities to Insights: Relation Extraction and Summarization15分钟

4个作业总计48分钟

Practice Quiz: Named Entity Recognition (NER)6分钟
Practice Quiz: Parsing & Dependency Trees6分钟
Practice Quiz: Information Extraction and Text Mining6分钟
Knowledge Check: Named Entity Recognition (NER) & Parsing30分钟

This module is designed to assess learners on the key concepts and techniques covered throughout the course. It includes a graded quiz that tests knowledge of NLP foundations, linguistic principles, text preprocessing, feature engineering, entity recognition, and parsing methods using both classical and deep learning approaches.

涵盖的内容

1个视频1篇阅读材料1个作业1个讨论话题

获得职业证书

将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

位教师

Edureka

98 门课程102,796 名学生

提供方

Edureka

从 Machine Learning 浏览更多内容

状态：免费试用
Edureka
Machine Learning and NLP Basics
课程
状态：免费试用
DeepLearning.AI
Natural Language Processing
专项课程
University of Colorado Boulder
Fundamentals of Natural Language Processing
课程
状态：免费试用
Packt
Natural Language Processing with Real-World Projects
专项课程

人们为什么选择 Coursera 来帮助自己实现职业发展

Felipe M.

自 2018开始学习的学生

''能够按照自己的速度和节奏学习课程是一次很棒的经历。只要符合自己的时间表和心情，我就可以学习。'

Jennifer J.

自 2020开始学习的学生

''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'

Larry W.

自 2021开始学习的学生

''如果我的大学不提供我需要的主题课程，Coursera 便是最好的去处之一。'

Chaitanya A.

''学习不仅仅是在工作中做的更好：它远不止于此。Coursera 让我无限制地学习。'

通过 Coursera Plus 开启新生涯

无限制访问 10,000+ 世界一流的课程、实践项目和就业就绪证书课程 - 所有这些都包含在您的订阅中

了解更多

通过在线学位推动您的职业生涯

获取世界一流大学的学位 - 100% 在线

探索学位

加入超过 3400 家选择 Coursera for Business 的全球公司

提升员工的技能，使其在数字经济中脱颖而出

了解更多

常见问题

NLP (Natural Language Processing) is a branch of artificial intelligence designed to help computers understand, interpret, and generate human language. It is an extensive field with many applications, such as machine translation, chatbots, text analysis, and sentiment analysis.

The key components of NLP are:

Natural Language Understanding (NLU): The process of mapping human language input to a representation that can be understood by the computer.

Natural Language Generation (NLG): The process of generating human language output from a representation that can be understood by the computer.

Some common applications of NLP are:

Machine Translation: The process of translating text from one language to another.

Chatbots: Interactive systems that can communicate with users in natural language.

Text Analysis: The process of extracting information and insights from text data.

Sentiment analysis: Determining the emotional tone of text.

Question Answering: The development of systems that are capable of responding to inquiries regarding a specific text or knowledge base.

Some common challenges in NLP include:

Ambiguity: Words and phrases can have multiple meanings, making it difficult for computers to understand the intended meaning.

Context: The meaning of words and phrases can vary depending on the context in which they are used.

Computational Complexity: Processing large amounts of text data can be computationally expensive.

Bias: NLP models can reflect the biases present in the data they are trained on.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.