Join Barton Poulson for an in-depth discussion in this video Anomaly detection in BigML, part of Data Science Foundations: Data Mining.
- When you go to BigML,…what you're going to need to do of course…is have your own account.…You can set that up for free for a small number of tasks.…And you need to go to your dashboard.…I'm gonna do that.…Then I'm going to import the data…by clicking on this one right here,…create a data source from a local file…because I have it saved on my desktop.…I want this one right here, anomaly data,…and there it is, I'm gonna click on that.…It's gonna show me the entire data set.…
Again, it lists the variables down the side…and the cases or instances across the top.…I'm going to create a data set from this.…Configure data set.…And so, what I'm going to do is I'm gonna just change…that name there.…And then I'm going to remove a few variables…I've been removing all the time anyhow.…I'm gonna remove the long name of this date…and I'm gonna come down here to the bottom…and remove these psych regions…and the demographic region and division.…So once I've got those done, I can hit create the data set.…
And remember, all the processing is done…
Barton Poulson covers data sources and types, the languages and software used in data mining (including R and Python), and specific task-based lessons that help you practice the most common data-mining techniques: text mining, data clustering, association analysis, and more. This course is an absolute necessity for those interested in joining the data science workforce, and for those who need to obtain more experience in data mining.
- Prerequisites for data mining
- Data mining using R, Python, Orange, and RapidMiner
- Data reduction
- Data clustering
- Anomaly detection
- Association analysis
- Regression analysis
- Sequence mining
- Text mining
Skill Level Beginner
Transitioning from Data Warehousing to Big Datawith Alan Simon1h 50m Intermediate
Big Data Foundations: Program Managementwith Alan Simon1h 11m Intermediate
2. Data Reduction
5. Anomaly Detection
6. Association Analysis
7. Regression Analysis
8. Sequential Patterns
9. Text Mining
Next steps1m 18s
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