Using BigML: Unsupervised Learning
Machine Learning 2020 | Intermediate
- 9 videos | 1h 22s
- Includes Assessment
- Earns a Badge
BigML includes various unsupervised learning models used to gain insights into your data. These insights can help make pivotal business decisions or act as a starting point to build supervised learning models. In this course, you'll build several unsupervised learning models and analyze the results they produce. You'll start by creating clusters from a dataset and examining how data points within a cluster share similarities. You'll move on to uncover associations in a dataset about items purchased on an e-commerce platform. Next, you'll apply topic modeling to extract the topics discussed in a collection of texts. Following this, you'll transform a dataset containing multiple fields into a handful of principal components using Principal Component Analysis, or PCA. Finally, you'll explore the detection of anomalies in your dataset.
WHAT YOU WILL LEARN
discover the key concepts covered in this coursegenerate clusters in your input data and analyze the properties of each clusterillustrate the features of the various clustering models that can be configured in BigMLcreate models from your cluster instances to identify the factors that affect cluster membershipidentify associations in a collection of transactions to find items purchased together
extract discussion topics in a collection of text-based product reviewsfind the principal components in a dataset containing several fieldsidentify the anomalies in a dataset using BigML's anomaly detection modelsummarize the key concepts covered in this course
IN THIS COURSE
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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