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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ranking-search-results-using-machine-learning/
课程评论：没有评论
本门课程《使用机器学习进行搜索结果排名》旨在教授学员如何利用机器学习技术和流行的Python编程语言以及Elasticsearch来优化搜索结果的排名。 **核心内容包括：** * **理解搜索排名：** 深入探讨搜索排名的基本原理，以及如何运用机器学习来提升搜索结果的相关性。 * **技术栈：** 课程将使用PyCharm和Python进行编程，并重点介绍LAMBDAMART、LAMBDANET和RANKNET等机器学习算法在搜索排名中的应用。 * **实战工具：** 将学习使用RankLib来训练排名模型，并利用Learning To Rank插件来配置和收集特征。 * **项目实践：** 学员将一步步构建一个实际的搜索排名应用程序，包括特征工程、模型训练和评估。 * **应用场景：** 课程还将探讨搜索结果排名在信息检索、社交平台等领域中的实际用例。 **课程亮点：** * **实战导向：** 教授学员掌握一项强大的技能，直接应用于实际问题。 * **免费工具：** 使用Python和Elasticsearch——这两种免费且易于学习的工具。 * **高需求技能：** 机器学习和搜索排名是当前热门且就业前景广阔的领域。 * **企业应用：** 介绍Google、Microsoft等大公司如何利用这些技术提升效率。 * **循序渐进：** 课程结构清晰，从基础介绍到实战演示，适合有一定编程基础（中级程序员）的学习者。 * **教学方法：** 采用直观的视觉化教学方法，将复杂概念分解为简单的步骤，提高学习效率和记忆力。 本课程将帮助您成为一名在信息检索领域中炙手可热的机器学习开发者。
Course DescriptionLearn ranking search results with the machine learning and popular programming language Python and Elastic Search.Build a strong foundation in Machine Learning with this tutorial for intermediate programmers.Understanding of Search RankingLeverage Machine Learning to rank search results Use PyCharm and Python for programmingUse LAMBDAMART, LAMBDANET, RANKNET Machine Learning Algorithms for ranking Search resultsUse RankLib to train ranking modelsUse Learning To Rank Plug to configure and collect featuresA Powerful Skill at Your Fingertips Learning the fundamentals of ranking search results puts a powerful and very useful tool at your fingertips. Python and Elastic Search are free, easy to learn, has excellent documentation.Jobs in machine learning area are plentiful, and being able to learn ranking search results with machine learning will give you a strong edge.Machine Learning is becoming very popular. Alexa, Siri, IBM Deep Blue and Watson are some famous example of Machine Learning application. Ranking search results is vital in information retrieval. Learning ranking search results with machine learning will help you become a machine learning developer which is in high demand.Big companies like Google, Bloomberg, Microsoft, and Yahoo already using ranking search results with machine learning in information retrieval and social platforms. They claimed that using Machine Learning and ranking search results has boosted productivity of entire company significantly.Content and Overview This course teaches you on how to rank search results using open source Python and Elastic Search framework. You will work along with me step by step to build following answersIntroduction to Search RankingIntroduction to Search Ranking using Machine LearningBuild an application step by step using Learning to Rank plug in, Elastic Search, Python and demo application from Open Source connectionsFeature EngineeringCollect FeaturesTrain ModelsEvaluate ModelsLearn use cases of ranking search results with machine learningWhat am I going to get from this course?Learn ranking search results and Machine Learning programming from professional trainer from your own desk.Over 10 lectures teaching you ranking search results programmingSuitable for intermediate programmers and ideal for users who learn faster when shown.Visual training method, offering users increased retention and accelerated learning.Breaks even the most complex applications down into simplistic steps.Note: Please note that I am using short documents in this example to illustrate concepts. You can use same code for longer documents as well.
