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Monkey_D_Law 2015-08-19 09:47 0 票支持; 0 票反对
这门课作为Introduction to Big Data with Apache Spark的后一门课，惊喜感不大。有个week内容完全重复，后面讲解了一些机器学习的内容，讲的还不错。感觉这两门课完全可以合在一起。
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开始时间: 04/22/2022 持续时间: 5 weeks
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所在平台: EdxArchive 课程类别: 计算机科学 大学或机构: UC BerkeleyX（加州大学伯克利分校） 授课老师： Ameet Talwalkar |
课程主页: https://www.edx.org/archive/scalable-machine-learning-uc-berkeleyx-cs190-1x
课程评论: 1 个评论
Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability and optimization. Learning algorithms enable a wide range of applications, from everyday tasks such as product recommendations and spam filtering to bleeding edge applications like self-driving cars and personalized medicine. In the age of ‘Big Data,’ with datasets rapidly growing in size and complexity and cloud computing becoming more pervasive, machine learning techniques are fast becoming a core component of large-scale data processing pipelines.
This course introduces the underlying statistical and algorithmic principles required to develop scalable real-world machine learning pipelines. We present an integrated view of data processing by highlighting the various components of these pipelines, including exploratory data analysis, feature extraction, supervised learning, and model evaluation. You will gain hands-on experience applying these principles using Apache Spark, a cluster computing system well-suited for large-scale machine learning tasks. You will implement scalable algorithms for fundamental statistical models (linear regression, logistic regression, matrix factorization, principal component analysis) while tackling key problems from domains such as online advertising and cognitive neuroscience.
This self-assessment document provides a short quiz, as well as online resources that review the relevant background material.
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Monkey_D_Law 2015-08-19 09:47 0 票支持; 0 票反对
这门课作为Introduction to Big Data with Apache Spark的后一门课，惊喜感不大。有个week内容完全重复，后面讲解了一些机器学习的内容，讲的还不错。感觉这两门课完全可以合在一起。
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