Big Data Applications: Machine Learning at Scale

开始时间: 08/08/2020 持续时间: Unknown

所在平台: Coursera

课程类别: 计算机科学

大学或机构: CourseraNew

   

课程主页: https://www.coursera.org/learn/machine-learning-applications-big-data

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Machine learning is transforming the world around us. To become successful, you’d better know what kinds of problems can be solved with machine learning, and how they can be solved. Don’t know where to start? The answer is one button away. During this course you will: - Identify practical problems which can be solved with machine learning - Build, tune and apply linear models with Spark MLLib - Understand methods of text processing - Fit decision trees and boost them with ensemble learning - Construct your own recommender system. As a practical assignment, you will - build and apply linear models for classification and regression tasks; - learn how to work with texts; - automatically construct decision trees and improve their performance with ensemble learning; - finally, you will build your own recommender system! With these skills, you will be able to tackle many practical machine learning tasks. We provide the tools, you choose the place of application to make this world of machines more intelligent. Special thanks to: - Prof. Mikhail Roytberg, APT dept., MIPT, who was the initial reviewer of the project, the supervisor and mentor of half of the BigData team. He was the one, who helped to get this show on the road. - Oleg Sukhoroslov (PhD, Senior Researcher at IITP RAS), who has been teaching MapReduce, Hadoop and friends since 2008. Now he is leading the infrastructure team. - Oleg Ivchenko (PhD student APT dept., MIPT), Pavel Akhtyamov (MSc. student at APT dept., MIPT) and Vladimir Kuznetsov (Assistant at P.G. Demidov Yaroslavl State University), superbrains who have developed and now maintain the infrastructure used for practical assignments in this course. - Asya Roitberg, Eugene Baulin, Marina Sudarikova. These people never sleep to babysit this course day and night, to make your learning experience productive, smooth and exciting.

大数据应用:大规模的机器学习:机器学习正在改变我们周围的世界。为了获得成功,您最好知道机器学习可以解决哪些问题,以及如何解决它们。不知道从哪里开始?答案是一个按钮。   在本课程中,您将: -确定可以通过机器学习解决的实际问题 -使用Spark MLLib构建,调整和应用线性模型 -了解文本处理方法 -适应决策树并通过整体学习促进决策树 -构建自己的推荐系统。   作为实际任务,您将 -为分类和回归任务建立并应用线性模型; -学习如何处理文本; -通过集成学习自动构建决策树并提高其性能; -最后,您将构建自己的推荐系统! 有了这些技能,您将能够解决许多实际的机器学习任务。   我们提供工具,您可以选择应用程序的位置来使机器世界变得更加智能。 特别感谢: -MIPT APT部门的Mikhail Roytberg教授,他是该项目的最初审阅者,也是BigData团队一半的主管和导师。他是帮助推动这场演出的人。 -Oleg Sukhoroslov(博士,IITP RAS高级研究员),自2008年以来一直在教授MapReduce,Hadoop和朋友。现在,他领导基础架构团队。 -奥列格·伊夫琴科(MITP博士,APT系学生),帕维尔·阿克赫蒂亚莫夫(Pavel Akhtyamov)(MITP系APT系硕士)和弗拉基米尔·库兹涅佐夫(PG德米多夫·雅罗斯拉夫尔州立大学的助教),他们已经开发并维护了用于本课程中的实际作业。 -Asya Roitberg,Eugene Baulin,Marina Sudarikova。这些人日夜不睡觉,这会让您的学习体验富有成效,顺畅而令人兴奋。

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课程简介

机器学习正在改变世界,通过这门课程,你将会学习到:识别实战中需要用机器学习算法解决的问题;通过Spark MLLib构建、调参、和应用线性模型;里面文本处理的方法;用决策树和Boost方法解决机器学习问题;构建自己的推荐系统。

课程标签

机器学习 Spark 大数据 MLLib 机器学习算法 机器学习实践 文本处理 线性模型 决策树 推荐系统 Boost

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