Do you work in data analysis? Learn how to mine data using R. Topics include dimensionality reduction, clustering, classification, association analysis, and more.
Overview
Syllabus
Introduction
- R for data mining
- Who should watch this course
- Exercise files
- Tools for data mining
- The CRISP-DM data mining model
- Privacy, copyright, and bias
- Validating results
- Dimensionality reduction overview
- Dataset: Handwritten digits
- PCA
- LDA
- t-SNE
- Challenge: PCA
- Solution: PCA
- Clustering overview
- Dataset: Penguins
- Hierarchical clustering
- K-means
- DBSCAN
- Challenge: K-means
- Solution: K-means
- Classification overview
- Dataset: Spambase
- K-nn
- Naive Bayes
- Decision trees
- Challenge: K-nn
- Solution: K-nn
- Association analysis overview
- Dataset: Groceries
- Apriori
- Eclat
- CBA
- Challenge: Apriori
- Solution: Apriori
- Time-series mining overview
- Dataset: AirPassengers
- Time-series decomposition
- ARIMA
- MLP
- Challenge: Decomposition
- Solution: Decomposition
- Text mining overview
- Dataset: The Iliad
- Sentiment analysis: Binary classification
- Sentiment analysis: Sentiment scoring
- Visualizing Word pairs
- Challenge: Sentiment scoring
- Solution: Sentiment scoring
- Next steps
Taught by
Barton Poulson
