中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.
Fundamentals of Natural Language Processing
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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作业
4 项作业
授课语言：英语（English）
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该课程共有4个模块
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7篇阅读材料1个作业
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
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
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
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。
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University of Colorado Boulder
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常见问题
Learners should be proficient in Python programming including the use of packages such as numpy, scikit-learn and pandas. Students should be proficient in data structures and basic topics in algorithm design, such as sorting and searching, dynamic programming, and algorithm analysis. Students should also have basic familiarity with introductory concepts from calculus, discrete probability, and linear algebra.
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.
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.
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