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Fundamentals of Natural Language Processing

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Fundamentals of Natural Language Processing

位教师：James Martin

2,532 人已注册

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4个模块

深入了解一个主题并学习基础知识。

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2 周在 10 小时一周

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4个模块

深入了解一个主题并学习基础知识。

13 条评论

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灵活的计划

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您将学到什么

Analyze corpora to develop effective lexicons using subword tokenization.
Develop language models that can assign probabilities to texts.
Design, implement, and evaluate the effectiveness of text classifiers using gradient-based learning techniques.
Design, implement and evaluate unsupervised methods for learning word embeddings.

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Classification Algorithms

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作业

5 项作业

授课语言：英语（English）

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该课程共有4个模块

The field of natural language processing (NLP) aims at getting computers to perform useful and interesting tasks with human language. This course introduces students to the 3 pillars underlying modern NLP: probabilistic language models, simple neural networks with a focus on gradient based learning, and vector-based meaning representations in the form of word embeddings. At the end of the course, students will be able to implement and analyze probabilistic language models based on N-grams, text classifiers using logistic regression and gradient-based learning, and vector-based approaches to word meaning and text classification.

This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://hua.dididi.sbs/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

This first week of Fundamentals of Natural Language Processing introduces the fundamental concepts of natural language processing (NLP), focusing on how computers process and analyze human language. You will explore key linguistic structures, including words and morphology, and learn essential techniques for text normalization and tokenization.

涵盖的内容

5个视频8篇阅读材料2个作业

5个视频总计56分钟

Meet Your Instructor 1分钟
Course Introduction 7分钟
Morphology 16分钟
Text Normalization 17分钟
Subword Tokenization 15分钟

8篇阅读材料总计141分钟

Course Updates and Accessibility Support 1分钟
Earn Academic Credit for Your Work! 10分钟
Course Support 10分钟
Assessment Expectations 5分钟
AI Citation and Acknowledgement 10分钟
Morphology 30分钟
Text Normalization 60分钟
Byte-Pair Encoding 15分钟

2个作业总计35分钟

AI Policy Quiz 5分钟
Quiz 1: Morphology and Tokenization 30分钟

This week explores foundational language modeling techniques, focusing on n-gram models and their role in statistical Natural Language Processing. You will learn how n-gram language models are constructed, smoothed, and evaluated for effectiveness.

涵盖的内容

4个视频4篇阅读材料1个作业1个编程作业

4个视频总计61分钟

Introducing Language Models 14分钟
N-Gram Based Language Models 16分钟
Smoothing N-Gram Language Models 22分钟
Evaluating Language Models 9分钟

4篇阅读材料总计80分钟

N-Gram Language Models: Introduction 10分钟
N-Gram Language Models: N-Grams 20分钟
N-Gram Language Models: Smoothing, Interpolation, and Backoff 20分钟
Evaluating Language Models 30分钟

1个作业总计30分钟

Quiz 2: Language Models 30分钟

1个编程作业总计180分钟

Constructing a Language Model 180分钟

This week introduces text classification and explores logistic regression as a powerful classification technique. You will learn how logistic regression models work, including key mathematical concepts such as the logit function, gradients, and stochastic gradient descent. The week also covers evaluation metrics for assessing classifier performance.

涵盖的内容

6个视频3篇阅读材料1个作业1个编程作业

6个视频总计91分钟

Introduction to Text Classification 10分钟
Logistic Regression 16分钟
Introducing the Logit 7分钟
Learning in Logistic Regression 19分钟
Learning Algorithms for Logistic Regression 17分钟
Evaluating Classifiers 21分钟

3篇阅读材料总计125分钟

Introduction to Text Classification 35分钟
Logistic Regression 60分钟
Evaluating Classifiers 30分钟

1个作业总计30分钟

Quiz 3: Logistic Regression 30分钟

1个编程作业总计180分钟

Sentiment Classification with Logistic Regression 180分钟

This final week explores how words can be represented as vectors in a high-dimensional space, allowing computational models to capture semantic relationships between words. You will learn about both sparse and dense vector representations, including TF-IDF, Pointwise Mutual Information (PMI), Latent Semantic Analysis (LSA), and Word2Vec. The module also covers techniques for evaluating and applying word embeddings.

涵盖的内容

7个视频4篇阅读材料1个作业1个编程作业

7个视频总计103分钟

Introduction to Vector Semantics 18分钟
Sparse Vector Representations: TF-IDF 15分钟
Sparse Vector Representations: Pointwise Mutual Information 11分钟
Latent Semantic Analysis 16分钟
Word2Vec 12分钟
Evaluating Word Embeddings 16分钟
Applying Word Embeddings 15分钟

4篇阅读材料总计125分钟

Introduction to Vector Semantics 30分钟
Sparse Vector Representations 30分钟
Word2Vec 20分钟
Evaluation and Application of Word Embeddings 45分钟

1个作业总计30分钟

Quiz 4: Vector-Space Semantics 30分钟

1个编程作业总计180分钟

Training and Applying Word Embeddings 180分钟

攻读学位

课程是 University of Colorado Boulder提供的以下学位课程的一部分。如果您被录取并注册，您已完成的课程可计入您的学位学习，您的学习进度也可随之转移。

位教师

授课教师评分

(6个评价)

James Martin

University of Colorado Boulder

1 门课程 2,532 名学生

提供方

University of Colorado Boulder

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