Join Barton Poulson for an in-depth discussion in this video Classification goals, part of Data Science Foundations: Data Mining.
- Classification is an attempt to place…new cases into the correct bucket.…So, think of it as simply choosing the…right bucket or box to put something in.…Now, this also highlights the difference…in machine learning.…Between what's called, unsupervised learning,…in which the data don't have any…"true" classes or criteria, you don't have…a final category for each case and the groups…are shaped or determined by similarity…among a whole range of variables,…as opposed to accuracy on a single outcome.…
So, that's unsupervised learning, or unlabeled learning…and then you can compare that with supervised learning,…or labeled learning, or labelled data.…This is where the data now have a true class…or outcome variable; they're actually supposed to…be in a particular category and now accuracy…is the guiding consideration in constructing…the models that go into the classification system.…This is also where we tie into the enormously…quickly growing field of machine learning and…all of the algorithms that go along with it.…
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
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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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