Join Barton Poulson for an in-depth discussion in this video Classification data, part of Data Science Foundations: Data Mining.
- [Voiceover] Our discussion of the algorithms…that go into classification is an interesting one…because I like to say…it's an embarrassment of riches.…There are so many choices…that are so dramatically different from each other…that sometimes it's kind of overwhelming…and hard to know exactly what to do.…So, for instance, there is k-nearest neighbors,…usually called k-NN.…There's naive Bayes.…There are decision trees, or if you have a whole…bunch of decision trees, you can have a random forest.…There are support vector machines.…
There are artificial neural networks.…There are k-means.…There's logistic regression.…And I'll tell you, if you've only taken…an introductory statistics course,…you might, maybe, have heard of one of those.…All the rest of this are incredibly powerful,…flexible alternatives for a lot of machine-learning tasks…of which classification is one of the primary ones.…Let me tell you a little bit about a few of these.…So, for instance, the first one, k-NN,…or k-nearest neighbors.…
The idea here is that your data exists in the…
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
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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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