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所在平台: Udemy |
课程主页: https://www.udemy.com/course/text-mining-with-machine-learning-and-python/
课程评论：没有评论
课程名称：使用机器学习和Python进行文本挖掘 课程概述：文本是数据科学领域中最活跃的研究和广泛传播的数据类型之一。随着机器学习和深度学习技术的新进展，构建基于文本源的优质数据产品变得可能。新兴的文本数据源不断涌现。该课程将帮助您建立自己的工具箱，掌握相关知识、软件包和代码片段，以便进行文本挖掘分析。您将从了解现代文本挖掘的基本原理开始，逐步学习与文本挖掘相关的一些激动人心的流程。课程将介绍如何使用机器学习从文本中提取有意义的信息，以及其中涉及的不同流程。学员将学习如何读取和处理文本特征，提取文本信息并使用预训练模型，同时深入探讨文本分类和实体提取与分类过程。您还将通过实际操作Skip-grams、CBOW和X2Vec等方法来探索词嵌入的过程，了解其他重要的文本挖掘流程。课程结束时，您将对使用机器学习进行文本挖掘的各个方面有深入的理解，并开始您的文本挖掘之旅。 作者简介：托马斯·德哈纳是位于比利时的食品科技初创公司FoodPairing的数据显示科学家，专注于利用机器学习、自然语言处理和人工智能捕捉与食品相关媒体的含义和趋势。他获得了根特大学的工业工程与运筹学硕士学位，并在数据分析和数据科学领域活跃了五年。除了本职工作外，托马斯还积极参与各种与数据科学相关的活动，如黑客松、Kaggle竞赛、聚会和公民数据科学项目。
Text is one of the most actively researched and widely spread types of data in the Data Science field today. New advances in machine learning and deep learning techniques now make it possible to build fantastic data products on text sources. New exciting text data sources pop up all the time. You'll build your own toolbox of know-how, packages, and working code snippets so you can perform your own text mining analyses. You'll start by understanding the fundamentals of modern text mining and move on to some exciting processes involved in it. You'll learn how machine learning is used to extract meaningful information from text and the different processes involved in it. You will learn to read and process text features. Then you'll learn how to extract information from text and work on pre-trained models, while also delving into text classification, and entity extraction and classification. You will explore the process of word embedding by working on Skip-grams, CBOW, and X2Vec with some additional and important text mining processes. By the end of the course, you will have learned and understood the various aspects of text mining with ML and the important processes involved in it, and will have begun your journey as an effective text miner. About the Author Thomas Dehaene is a Data Scientist at FoodPairing, a Belgium-based Food Technology scale-up that uses advanced concepts in Machine Learning, Natural Language Processing, and AI in general to capture meaning and trends from food-related media. He obtained his Master of Science degree in Industrial Engineering and Operations Research at Ghent University, before moving his career into Data Analytics and Data Science, in which he has been active for the past 5 years. In addition to his day job, Thomas is also active in numerous Data Science-related activities such as Hackathons, Kaggle competitions, Meetups, and citizen Data Science projects.
