Lynda - Machine Learning and AI Foundations - Clustering and Association - 磁力狗

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Lynda - Machine Learning and AI Foundations - Clustering and Association的二维码

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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