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IGTC01DA - Data Science Foundations Data Mining in R (EN)
01 - Introduction
01 - R for data mining (2:35)
02 - Who should watch this course (0:49)
03 - Exercise files (0:52)
Ex_Files_Data_Science_R
Ex_Files_Data_Science_R
02 - 1. Preliminaries
02 - The CRISP-DM data mining model (3:20)
01 - Tools for data mining (6:06)
03 - Privacy, copyright, and bias (4:26)
04 - Validating results (6:15)
03 - 2. Dimensionality Reduction
02 - Dataset Handwritten digits (5:57)
01 - Dimensionality reduction overview (6:35)
03 - PCA (5:33)
04 - LDA (8:06)
05 - t-SNE (3:56)
06 - Challenge PCA (1:28)
07 - Solution PCA (4:23)
04 - 3. Clustering
01 - Clustering overview (6:53)
02 - Dataset Penguins (2:40)
03 - Hierarchical clustering (7:27)
04 - K-means (4:16)
05 - DBSCAN (6:19)
06 - Challenge K-means (1:37)
07 - Solution K-means (5:10)
05 - 4. Classification
01 - Classification overview (6:10)
02 - Dataset Spambase (5:00)
03 - K-nn (7:20)
04 - Naive Bayes (6:12)
05 - Decision trees (7:01)
06 - Challenge K-nn (4:07)
07 - Solution K-nn (3:48)
06 - 5. Association Analysis
01 - Association analysis overview (6:02)
02 - Dataset Groceries (1:59)
03 - Apriori (4:53)
04 - Eclat (3:43)
05 - CBA (6:48)
06 - Challenge Apriori (2:08)
07 - Solution Apriori (2:26)
07 - 6. Time-Series Mining
01 - Time-series mining overview (4:23)
02 - Dataset AirPassengers (3:04)
03 - Time-series decomposition (5:54)
04 - ARIMA (6:59)
05 - MLP (7:08)
06 - Challenge Decomposition (2:29)
07 - Solution Decomposition (3:39)
08 - 7. Text Mining
02 - Dataset The Iliad (2:39)
01 - Text mining overview (4:34)
03 - Sentiment analysis Binary classification (6:24)
04 - Sentiment analysis Sentiment scoring (7:24)
05 - Visualizing Word pairs (6:36)
06 - Challenge Sentiment scoring (1:11)
07 - Solution Sentiment scoring (4:13)
09 - Conclusion
01 - Next steps (2:36)
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01 - Text mining overview
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