Join Barton Poulson for an in-depth discussion in this video Text mining in Python, part of Data Science Foundations: Data Mining.
- [Instructor] We're going to begin…by installing a number of packages.…Very familiar ones,…but one important new one,…and that's nltk,…which is for Natural Language Toolkit.…This is actually a very very large package…that's extremely powerful…for dealing with unstructured texts.…Things like novels.…Things like magazine articles, or blog posts, or tweets,…and it serves as one of the major reasons…for using Python for data mining.…The availability of the Natural Language Toolkit.…We're gonna do a very modest analysis using it,…but let's start by installing these packages.…
Now, I have them installed already,…so I can see that we're good to go here.…Then, we need to load them.…Now, these ones at the top, codecs, re,…which is for regular expressions,…copy, and collections.…Those are already in Python,…but we have to call those ones up.…We're gonna use some special functions from those.…Then, we have several from Natural Language Toolkit.…Let's run those, and then get them available.…That's our matplotlib font cache warning…
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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