This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.
IBM课程信息
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中级
完成时间大约为9 小时
英语（English）
字幕：英语（English）
您将获得的技能
Dimensionality ReductionUnsupervised LearningCluster AnalysisK Means ClusteringPrincipal Component Analysis (PCA)
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为9 小时
英语（English）
字幕：英语（English）
提供方
IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
教学大纲 - 您将从这门课程中学到什么
完成时间为 2 小时
Introduction to Unsupervised Learning and K Means
This module introduces Unsupervised Learning and its applications. One of the most common uses of Unsupervised Learning is clustering observations using k-means. In this module you become familiar with the theory behind this algorithm, and put it in practice in a demonstration.
完成时间为 2 小时
11 个视频 （总计 62 分钟）, 2 个阅读材料, 3 个测验
11 个视频
Introduction to Unsupervised Learning - Part 18分钟
Introduction to Unsupervised Learning - Part 24分钟
Introduction to Clustering1分钟
K-Means - Part 13分钟
K-Means - Part 23分钟
K-Means - Part 34分钟
K-Means - Part 44分钟
K Means Notebook - Part 19分钟
K Means Notebook - Part 26分钟
K Means Notebook - Part 313分钟
2 个阅读材料
K Means Demo (Activity)10分钟
Summary10分钟
3 个练习
Introduction to Unsupervised Learning10分钟
K Means Clustering10分钟
End of Module20分钟
完成时间为 4 小时
Selecting a clustering algorithm
In this module you become familiar with some of the computational hurdles around clustering algorithms, and how different clustering implementations try to overcome them. After a brief recapitulation of common clustering algorithms, you will learn how to compare them and select the clustering technique that best suits your data.
完成时间为 4 小时
16 个视频 （总计 143 分钟）, 3 个阅读材料, 4 个测验
16 个视频
Distance Metrics - Part 26分钟
Curse of Dimensionality Notebook - Part 111分钟
Curse of Dimensionality Notebook - Part 212分钟
Curse of Dimensionality Notebook - Part 312分钟
Curse of Dimensionality Notebook - Part 410分钟
Hierarchical Agglomerative Clustering - Part 13分钟
Hierarchical Agglomerative Clustering - Part 29分钟
DBSCAN - Part 15分钟
DBSCAN - Part 28分钟
Mean Shift9分钟
Comparing Algorithms11分钟
Clustering Notebook - Part 114分钟
Clustering Notebook - Part 26分钟
Clustering Notebook - Part 37分钟
Clustering Notebook - Part 411分钟
3 个阅读材料
Curse of Dimensionality Demo (Activity)10分钟
Clustering Demo (Activity)10分钟
Summary10分钟
4 个练习
Distance Metrics10分钟
Clustering Algorithms10分钟
Comparing Clustering Algorithms10分钟
End of Module10分钟
完成时间为 4 小时
Dimensionality Reduction
This module introduces dimensionality reduction and Principal Component Analysis, which are powerful techniques for big data, imaging, and pre-processing data. At the end of this module, you will have all the tools in your toolkit to highlight your Unsupervised Learning abilities in your final project.
完成时间为 4 小时
9 个视频 （总计 88 分钟）, 3 个阅读材料, 4 个测验
9 个视频
Dimensionality Reduction - Part 26分钟
PCA Notebook - Part 111分钟
PCA Notebook - Part 212分钟
PCA Notebook - Part 311分钟
Non Negative Matrix Factorization9分钟
Non Negative Matrix Factorization Notebook - Part 18分钟
Non Negative Matrix Factorization Notebook - Part 26分钟
Dimensionality Reduction Imaging Example7分钟
3 个阅读材料
Principal Component Analysis (Activity)2分钟
Non Negative Matrix Factorization (Activity)2分钟
Summary10分钟
3 个练习
Dimensionality Reduction5分钟
Non Negative Matrix Factorization10分钟
End of Module10分钟
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