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所在平台: Udemy |
课程主页: https://www.udemy.com/course/introduction-to-natural-language-processing-in-python-2024/
课程评论：没有评论
课程名称：Python自然语言处理入门 [2024] 课程概述：自然语言处理（NLP）是一个迅速发展的领域，涉及语言学、计算机科学和人工智能。本课程提供了一个全面的自然语言处理入门，通过Python编程语言涵盖基本概念、技术和工具，帮助学生分析和处理人类语言数据。课程中，学生将学习如何利用Python库（如NLTK、spaCy和scikit-learn）完成多种NLP任务，包括分词、词干提取、词形还原、词性标注、命名实体识别、情感分析、文本分类和语言建模。 课程开始时将介绍基本的NLP概念和技术，包括文本预处理、特征提取和向量化。学生将学习如何清理和预处理文本数据，将文本转换为适合机器学习模型的数值表示，并使用词云和频率分布等技术可视化文本数据。接着，课题将深入探讨更高级的NLP主题，包括句法和语义分析、语法解析和词嵌入。学生将探索分析句子和文档结构及含义的技术。 此外，课程还将介绍NLP在各种领域中的实际应用，例如信息检索、问答系统、机器翻译和聊天机器人开发，学生将学习如何使用真实数据集构建和评估NLP模型并使用相应的指标和技术评估模型的表现。 课程结束时，学生将掌握NLP的基本原理和技术，并能够运用这些知识来解决现实问题。无论你是初学者还是有经验的Python程序员，该课程都将为你提供必要的知识和技能，以便开始处理自然语言数据并构建智能NLP应用。 课程大纲包括：课程介绍、自然语言处理概述、文本预处理、分词技术及其实现、正则表达式、词干提取与词形还原、停用词移除、N-grams、文本表示、Word2vec、特征提取、文本分类、机器学习及其实现、层次聚类与K-means聚类等。 授课方式：课程将通过讲座、演示、动手练习和项目工作等形式进行。学生将访问在线资源，包括讲义、代码示例和额外阅读材料。教师指导会与自主学习模块和小组讨论相结合。 认证：完成课程后，学生将获得结业证书，证明其在Python自然语言处理方面的能力。 加入我们，探索自然语言处理的迷人世界，发现构建能理解和与人类语言数据互动的智能应用的无尽可能。立即注册，迈出掌握Python自然语言处理艺术的第一步！
Natural Language Processing (NLP) is a rapidly evolving field at the intersection of linguistics, computer science, and artificial intelligence. This course provides a comprehensive introduction to NLP using the Python programming language, covering fundamental concepts, techniques, and tools for analyzing and processing human language data.Throughout the course, students will learn how to leverage Python libraries such as NLTK (Natural Language Toolkit), spaCy, and scikit-learn to perform various NLP tasks, including tokenization, stemming, lemmatization, part-of-speech tagging, named entity recognition, sentiment analysis, text classification, and language modeling.The course begins with an overview of basic NLP concepts and techniques, including text preprocessing, feature extraction, and vectorization. Students will learn how to clean and preprocess text data, convert text into numerical representations suitable for machine learning models, and visualize textual data using techniques such as word clouds and frequency distributions.Next, the course covers more advanced topics in NLP, including syntactic and semantic analysis, grammar parsing, and word embeddings. Students will explore techniques for analyzing the structure and meaning of sentences and documents, including dependency parsing, constituency parsing, and semantic role labeling.The course also introduces students to practical applications of NLP in various domains, such as information retrieval, question answering, machine translation, and chatbot development. Students will learn how to build and evaluate NLP models using real-world datasets and evaluate their performance using appropriate metrics and techniques.By the end of the course, students will have a solid understanding of the fundamental principles and techniques of NLP and the ability to apply them to solve real-world problems using Python. Whether you are a beginner or an experienced Python programmer, this course will provide you with the knowledge and skills you need to start working with natural language data and build intelligent NLP applications.Course Outline:Introduction Course strucureHow to make out of this courseOverview of natural language processingText pre-processingTokenization techniques (word-level, sentence-level) and its implementationRegular expression and its implementationTreebank tokenizer and its implementationTweetTokenizer and its implementationStemming and its implementationWordNet Lemmatizer and its implementationspacy Lemmatizer and its implementationThe introduction and implementation of stop word removalThe introduction and implementation of Case foldingIntroduction and implementation of N-gramsText RepresentationIntroduction to Word2vec and implementationskip-gram implementationBag of word implementationHow to perform basic feature extraction methodsWhat are types of dataText cleaning and tokenization practice.How to perform text tokenization using keras and TextBlobSingularizing and pluralizing words and language translationWhat does feature extraction mean in natural language processingImplementation of feature extraction in natural language processing.Introduction to Zipf's Law and implementationIntroduction to TF-IDF and implementationfeature engineeringIntroduction to WordCloud and its implementationspaCy overview and implementationIntroduction to spaCyTokenization Implementationlemmatization ImplementationText Classifier ImplementationIntroduction to Machine learningIntroduction to Hierarchical Clustering and implementationintroduction to K-means Clustering and implementationIntroduction to Text Classification and implementation introduction to tree methods and implementationintroduction to Removing Correlated Features and implementationintroduction to Dimensionality Reduction and implementationMode of Instruction:The course will be delivered through a combination of lectures, demonstrations, hands-on exercises, and project work.Students will have access to online resources, including lecture slides, code examples, and additional reading materials.Instructor-led sessions will be supplemented with self-paced learning modules and group discussions.certification:Upon successful completion of the course, students will receive a certificate of completion, indicating their proficiency in natural language processing with Python.Join us on a journey into the fascinating world of natural language processing and discover the endless possibilities for building intelligent applications that can understand and interact with human language data. Enroll now and take the first step towards mastering the art of NLP with Python!
