Join Barton Poulson for an in-depth discussion in this video Sequence mining in Python, part of Data Science Foundations: Data Mining.
- Now the kind of sequence mining that we're going to do…is a specific kind called hidden Markov chains.…In particular, we're looking for a state changes…where people go from one particular way of reacting,…and they switch over to another different way.…The idea here is based on the psychological research.…There's a quick and easy or a slow and intensive…way of thinking about judgments,…and these are very small differences,…but the idea is that as the reward system changes…that people's preference for doing…the quick and easy versus the slow and intensive…might change as well.…
That is, they switch from one state to another.…A hidden Markov model is going to be one of the best ways…of looking for that.…Now to do this, we're going to install…our usual suspects for working with data pandas in NumPy,…but we're adding at the bottom HMM learn,…which is for Hidden Markov Model Learn.…We'll install those.…You probably don't have HMM Learn installed already.…Then we're going to import them,…and from HMM Learn, we're using one called…
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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