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所在平台: Udemy |
课程主页: https://www.udemy.com/course/text-mining-and-natural-language-processing-in-python/
课程评论：没有评论
课程名称：Python中的文本挖掘与自然语言处理 课程概述：您是否想分析产品评论或社交媒体帖子，以了解它们是积极还是消极的？您是否希望让计算机理解自然语言？那么这个课程正适合您！我们将介绍自然语言处理（NLP）的基本理论基础，并直接应用于Python。在这个信息化时代，企业和组织越来越需要跟踪大量关于其品牌或产品评论的社交媒体帖子。NLP中有一个重要领域叫做情感分析，旨在自动化这个过程。最后，您将学习如何构建一个深度学习模型，处理文本并预测其情感是正面还是负面。如果您对如何构建这样的模型感兴趣，那么这个课程将满足您的需求！ 在课程中，您将了解NLP和文本挖掘的基础知识，并学习如何在Python中实现这些知识。课程将指导您使用spaCy或NLTK等Python模块直接实现所学的方法。除了学习NLP的基本规则和常用方法外，您还会接触到最先进的Transformer模型。最终，您将结合所学知识构建一个功能齐全的深度学习模型，能够处理文本输入并预测情感。通过本课程，您将全面掌握文本预处理的各个步骤，将它们组合成数据集，并在TensorFlow中构建深度学习模型。 讲师介绍：课程由经验丰富的机器学习工程师和大学教师Niklas Lang授课。他目前在一家德国IT系统公司工作，拥有处理来自电商网站、产品描述和在线评论等文本数据的丰富经验，并将其转化为强大的机器学习模型。此外，他还曾在大学层面教授数据科学和商业智能课程。 课程亮点： - Jupyter Notebook和Python模块管理入门 - 自然语言及其应用的介绍 - Python中的文本预处理技术详解 - 特征工程方法概述，包括Word2Vec、词袋模型或BERT嵌入 - 卷积神经网络在分类任务中的深入解释 - 在TensorFlow中实现情感分析任务的机器学习模型 - 深度学习模型的构建、编译和训练过程 立即加入课程吧！
Do You Want to Analyse Product Reviews or Social Media Posts to see whether they are positive or negative?Do you want to be able to make Computers understand Natural Language?Then this course is just right for you! We will go over the basic, theoretical foundations of Natural Language Processing (NLP) and directly apply them in Python. It becomes ever more important for companies and organizations to keep track of large amounts of social media posts concerning their brand or product reviews. In NLP there is a whole field called sentiment analysis, that tries to automate this process. In the end, a Deep Learning model can then process a text and predict whether it's a positive or negative review. If you are curious about how to build such a model, then this course is just right for you!Get to know the Basics of NLP & Text Mining and learn how to implement it in Python:My course will help you implement the learned methods directly in Python modules like spaCy or NLTK. Besides learning the ground rules of NLP and common methods, you will even deal with so-called Transformer models, which are state-of-the-art in Natural Language Processing. In the end, you will combine your gained knowledge to build up a functioning Deep Learning Model that can take text as input and predict a sentiment. With this powerful course, you'll know it all: applying different steps of text preprocessing, combining it in datasets, and building a Deep Learning Model in TensorFlow. Learn from an experienced Machine Learning Engineer and University Teacher: My name is Niklas Lang and I am a Machine Learning Engineer, currently working for a German IT System House. I have experience in working with kinds of textual data arising from our e-commerce website, product descriptions, or online reviews which we turn into powerful and working Machine Learning models. Besides that, I already taught courses at University level for Data Science as well as Business Intelligence. Here is what you will get: Introduction to Jupyter Notebooks and Python Module ManagementIntroduction to Natural Languages and NLP ApplicationsIn-Detail Text Preprocessing Techniques in PythonOverview of Feature Engineering Approaches like Word2Vec, Bag of Words, or BERT EmbeddingsIn-Depth Explanation on Convolutional Neural Networks for Classification TasksImplementing Machine Learning Model for Sentiment Analysis Task in TensorFlowGetting to know the Process of Building, Compiling and Training a Deep Learning Model in PythonJoin the course now!
