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文档列表
Exercise Files/Ex_Files_Machine_Learning_AI_Clustering.zip
26.2 MB
6.5. Anomaly Detection/34.Using SOM for anomaly detection.mp4
22.8 MB
6.5. Anomaly Detection/32.The k = 1 trick.mp4
21.7 MB
5.4. Cluster Methods for Categorical Variables/31.A self organizing map example.mp4
21.3 MB
4.3. Visualizing and Reporting Cluster Solutions/24.Line graphs.mp4
20.4 MB
2.1. What Is Cluster Analysis/05.Looking at the data with a 2D scatter plot.mp4
19.6 MB
7.6. Association Rules and Sequence Detection/36.Running association rules.mp4
19.5 MB
2.1. What Is Cluster Analysis/06.Understanding hierarchical cluster analysis.mp4
19.4 MB
7.6. Association Rules and Sequence Detection/38.Interpreting association rules.mp4
19.0 MB
5.4. Cluster Methods for Categorical Variables/25.Relating clusters to categories statistically.mp4
18.6 MB
5.4. Cluster Methods for Categorical Variables/27.Running a multiple correspondence analysis.mp4
17.8 MB
2.1. What Is Cluster Analysis/09.Methods for measuring distance.mp4
16.7 MB
7.6. Association Rules and Sequence Detection/39.Putting association rules to use.mp4
16.4 MB
6.5. Anomaly Detection/33.Anomaly detection algorithms.mp4
15.2 MB
7.6. Association Rules and Sequence Detection/41.Sequence detection.mp4
15.1 MB
3.2. K-Means/15.Interpreting cluster analysis output.mp4
15.1 MB
4.3. Visualizing and Reporting Cluster Solutions/22.Summarizing cluster means in a table.mp4
14.9 MB
5.4. Cluster Methods for Categorical Variables/29.Using cluster analysis and decision trees together.mp4
14.9 MB
3.2. K-Means/19.Finding optimum value for k - k = 4.mp4
14.9 MB
5.4. Cluster Methods for Categorical Variables/30.A BIRCH_two-step example.mp4
13.7 MB
3.2. K-Means/18.Finding optimum value for k - k = 3.mp4
13.2 MB
3.2. K-Means/20.Finding optimum value for k - k = 5.mp4
12.8 MB
2.1. What Is Cluster Analysis/10.What is k-nearest neighbors.mp4
12.7 MB
3.2. K-Means/17.Which cases should be used with k-means.mp4
11.7 MB
5.4. Cluster Methods for Categorical Variables/28.Interpreting a perceptual map.mp4
11.6 MB
2.1. What Is Cluster Analysis/07.Running hierarchical cluster analysis.mp4
11.2 MB
2.1. What Is Cluster Analysis/08.Interpreting a dendrogram.mp4
10.9 MB
3.2. K-Means/14.Running a k-means cluster analysis.mp4
10.9 MB
3.2. K-Means/21.What the best solution.mp4
10.4 MB
1.Introduction/04.What is unsupervised machine learning.mp4
9.8 MB
4.3. Visualizing and Reporting Cluster Solutions/23.Traffic Light feature in Excel.mp4
9.7 MB
3.2. K-Means/13.Interpreting a box plot.mp4
9.0 MB
3.2. K-Means/12.Which variables should be used with k-means.mp4
9.0 MB
7.6. Association Rules and Sequence Detection/35.Intro to association rules and sequence analysis.mp4
7.6 MB
5.4. Cluster Methods for Categorical Variables/26.Relating clusters to categories visually.mp4
6.9 MB
3.2. K-Means/11.How does k-means work.mp4
6.5 MB
1.Introduction/01.Welcome.mp4
6.3 MB
7.6. Association Rules and Sequence Detection/40.Comparing clustering and association rules.mp4
6.1 MB
7.6. Association Rules and Sequence Detection/37.Some association rules terminology.mp4
4.7 MB
1.Introduction/03.Using the exercise files.mp4
4.2 MB
1.Introduction/02.What you should know.mp4
3.4 MB
3.2. K-Means/16.What does silhouette mean.mp4
3.4 MB
8.Conclusion/42.Next steps.mp4
2.5 MB
5.4. Cluster Methods for Categorical Variables/29.Using cluster analysis and decision trees together.en.srt
15.2 kB
4.3. Visualizing and Reporting Cluster Solutions/24.Line graphs.en.srt
12.2 kB
7.6. Association Rules and Sequence Detection/38.Interpreting association rules.en.srt
11.5 kB
6.5. Anomaly Detection/32.The k = 1 trick.en.srt
11.3 kB
3.2. K-Means/13.Interpreting a box plot.en.srt
11.3 kB
5.4. Cluster Methods for Categorical Variables/31.A self organizing map example.en.srt
11.3 kB
5.4. Cluster Methods for Categorical Variables/25.Relating clusters to categories statistically.en.srt
11.0 kB
6.5. Anomaly Detection/34.Using SOM for anomaly detection.en.srt
10.1 kB
3.2. K-Means/19.Finding optimum value for k - k = 4.en.srt
9.8 kB
1.Introduction/04.What is unsupervised machine learning.en.srt
9.7 kB
3.2. K-Means/15.Interpreting cluster analysis output.en.srt
9.6 kB
2.1. What Is Cluster Analysis/05.Looking at the data with a 2D scatter plot.en.srt
9.6 kB
7.6. Association Rules and Sequence Detection/36.Running association rules.en.srt
9.6 kB
2.1. What Is Cluster Analysis/09.Methods for measuring distance.en.srt
9.5 kB
7.6. Association Rules and Sequence Detection/41.Sequence detection.en.srt
9.1 kB
5.4. Cluster Methods for Categorical Variables/27.Running a multiple correspondence analysis.en.srt
8.7 kB
2.1. What Is Cluster Analysis/10.What is k-nearest neighbors.en.srt
8.7 kB
2.1. What Is Cluster Analysis/06.Understanding hierarchical cluster analysis.en.srt
8.6 kB
3.2. K-Means/18.Finding optimum value for k - k = 3.en.srt
8.4 kB
4.3. Visualizing and Reporting Cluster Solutions/22.Summarizing cluster means in a table.en.srt
8.4 kB
7.6. Association Rules and Sequence Detection/35.Intro to association rules and sequence analysis.en.srt
8.0 kB
3.2. K-Means/20.Finding optimum value for k - k = 5.en.srt
8.0 kB
7.6. Association Rules and Sequence Detection/39.Putting association rules to use.en.srt
8.0 kB
3.2. K-Means/17.Which cases should be used with k-means.en.srt
7.8 kB
5.4. Cluster Methods for Categorical Variables/30.A BIRCH_two-step example.en.srt
7.7 kB
6.5. Anomaly Detection/33.Anomaly detection algorithms.en.srt
7.0 kB
2.1. What Is Cluster Analysis/07.Running hierarchical cluster analysis.en.srt
6.7 kB
2.1. What Is Cluster Analysis/08.Interpreting a dendrogram.en.srt
6.2 kB
4.3. Visualizing and Reporting Cluster Solutions/23.Traffic Light feature in Excel.en.srt
5.8 kB
3.2. K-Means/21.What the best solution.en.srt
5.7 kB
3.2. K-Means/14.Running a k-means cluster analysis.en.srt
5.4 kB
7.6. Association Rules and Sequence Detection/37.Some association rules terminology.en.srt
5.3 kB
3.2. K-Means/12.Which variables should be used with k-means.en.srt
5.2 kB
5.4. Cluster Methods for Categorical Variables/28.Interpreting a perceptual map.en.srt
5.2 kB
5.4. Cluster Methods for Categorical Variables/26.Relating clusters to categories visually.en.srt
5.0 kB
7.6. Association Rules and Sequence Detection/40.Comparing clustering and association rules.en.srt
4.6 kB
1.Introduction/02.What you should know.en.srt
4.0 kB
3.2. K-Means/16.What does silhouette mean.en.srt
3.7 kB
3.2. K-Means/11.How does k-means work.en.srt
3.3 kB
8.Conclusion/42.Next steps.en.srt
2.8 kB
1.Introduction/03.Using the exercise files.en.srt
2.2 kB
1.Introduction/01.Welcome.en.srt
1.3 kB
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Foundations
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