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所在平台: Udemy |
课程主页: https://www.udemy.com/course/the-ultimate-beginners-guide-to-natural-language-processing/
课程评论：没有评论
课程名称：《终极初学者自然语言处理指南》 概述：自然语言处理（NLP）是人工智能的一个子领域，旨在使计算机能够理解人类的书面和口头语言。实际应用的例子包括语言翻译器、文本转语音或语音转文本、聊天机器人、自动问答系统、图像描述的自动生成、视频字幕的生成以及句子情感分类等。学习这一领域可以为当今及未来的需求提供真正的解决方案。该课程旨在帮助希望在自然语言处理领域成长或开创新职业的学员，使用spaCy和NLTK（自然语言工具包）库以及Python编程语言。SpaCy专注于生产与实际环境的使用，能够处理大量数据，提取信息、理解自然语言，并为深度学习模型的后期使用预处理文本。 课程分为三个部分： 1. 第一部分将介绍自然语言处理的基本概念，包括词性标注、词形还原、词干提取、命名实体识别、停用词、依存句法分析、词和句子相似度及分词等。 2. 第二部分将涵盖更高级的主题，如预处理函数、词云、文本摘要、关键词搜索、词袋模型、TF-IDF（词频-逆文档频率）和余弦相似度。我们还将模拟一个能回答任何主题问题的聊天机器人。 3. 第三部分将创建一个情感分类器，使用真实的Twitter数据集。我们将利用NLTK、TF-IDF以及spaCy库实现该分类器。 这可以视为自然语言处理的入门课程，完成后您可以继续学习更高级的材料。如果您之前对自然语言处理没有了解，这门课程非常适合您！课程结束时，您将具备开发一些简单项目的实践基础，并能够参加更高级的课程。课堂上，代码将逐步在Google Colab上实现，以确保您在本地计算机上不会遇到安装或配置软件的问题。
The area of Natural Language Processing (NLP) is a subarea of Artificial Intelligence that aims to make computers capable of understanding human language, both written and spoken. Some examples of practical applications are: translators between languages, translation from text to speech or speech to text, chatbots, automatic question and answer systems (Q & A), automatic generation of descriptions for images, generation of subtitles in videos, classification of sentiments in sentences, among many others! Learning this area can be the key to bringing real solutions to present and future needs!Based on that, this course was designed for those who want to grow or start a new career in Natural Language Processing, using the spaCy and NLTK (Natural Language Toolkit) libraries and the Python programming language! SpaCy was developed with the focus on use in production and real environments, so it is possible to create applications that process a lot of data. It can be used to extract information, understand natural language and even preprocess texts for later use in deep learning models.The course is divided into three parts:In the first one, you will learn the most basic natural language processing concepts, such as: part-of-speech, lemmatization, stemming, named entity recognition, stop words, dependency parsing, word and sentence similarity and tokenizationIn the second part, you will learn more advanced topics, such as: preprocessing function, word cloud, text summarization, keyword search, bag of words, TF-IDF (Term Frequency - Inverse Document Frequency), and cosine similarity. We will also simulate a chatbot that can answer questions about any subject you want!Finally, in the third and last part of the course, we will create a sentiment classifier using a real Twitter dataset! We will implement the classifier using NLTK, TF-IDF and also the spaCy libraryThis can be considered the first course in natural language processing, and after completing it, you can move on to more advanced materials. If you have never heard about natural language processing, this course is for you! At the end you will have the practical background to develop some simple projects and take more advanced courses. During the lectures, the code will be implemented step by step using Google Colab, which will ensure that you will have no problems with installations or configurations of software on your local machine.
