Lynda - Machine Learning and AI Foundations - Clustering and Association
File List
- Exercise Files/Ex_Files_Machine_Learning_AI_Clustering.zip 25.0 MB
- 6.5. Anomaly Detection/34.Using SOM for anomaly detection.mp4 21.7 MB
- 6.5. Anomaly Detection/32.The k = 1 trick.mp4 20.7 MB
- 5.4. Cluster Methods for Categorical Variables/31.A self organizing map example.mp4 20.3 MB
- 4.3. Visualizing and Reporting Cluster Solutions/24.Line graphs.mp4 19.4 MB
- 2.1. What Is Cluster Analysis/05.Looking at the data with a 2D scatter plot.mp4 18.7 MB
- 7.6. Association Rules and Sequence Detection/36.Running association rules.mp4 18.6 MB
- 2.1. What Is Cluster Analysis/06.Understanding hierarchical cluster analysis.mp4 18.5 MB
- 7.6. Association Rules and Sequence Detection/38.Interpreting association rules.mp4 18.2 MB
- 5.4. Cluster Methods for Categorical Variables/25.Relating clusters to categories statistically.mp4 17.7 MB
- 5.4. Cluster Methods for Categorical Variables/27.Running a multiple correspondence analysis.mp4 17.0 MB
- 2.1. What Is Cluster Analysis/09.Methods for measuring distance.mp4 16.0 MB
- 7.6. Association Rules and Sequence Detection/39.Putting association rules to use.mp4 15.6 MB
- 6.5. Anomaly Detection/33.Anomaly detection algorithms.mp4 14.5 MB
- 7.6. Association Rules and Sequence Detection/41.Sequence detection.mp4 14.4 MB
- 3.2. K-Means/15.Interpreting cluster analysis output.mp4 14.4 MB
- 4.3. Visualizing and Reporting Cluster Solutions/22.Summarizing cluster means in a table.mp4 14.2 MB
- 5.4. Cluster Methods for Categorical Variables/29.Using cluster analysis and decision trees together.mp4 14.2 MB
- 3.2. K-Means/19.Finding optimum value for k - k = 4.mp4 14.2 MB
- 5.4. Cluster Methods for Categorical Variables/30.A BIRCH_two-step example.mp4 13.1 MB
- 3.2. K-Means/18.Finding optimum value for k - k = 3.mp4 12.6 MB
- 3.2. K-Means/20.Finding optimum value for k - k = 5.mp4 12.2 MB
- 2.1. What Is Cluster Analysis/10.What is k-nearest neighbors.mp4 12.1 MB
- 3.2. K-Means/17.Which cases should be used with k-means.mp4 11.1 MB
- 5.4. Cluster Methods for Categorical Variables/28.Interpreting a perceptual map.mp4 11.1 MB
- 2.1. What Is Cluster Analysis/07.Running hierarchical cluster analysis.mp4 10.7 MB
- 2.1. What Is Cluster Analysis/08.Interpreting a dendrogram.mp4 10.4 MB
- 3.2. K-Means/14.Running a k-means cluster analysis.mp4 10.4 MB
- 3.2. K-Means/21.What the best solution.mp4 9.9 MB
- 1.Introduction/04.What is unsupervised machine learning.mp4 9.3 MB
- 4.3. Visualizing and Reporting Cluster Solutions/23.Traffic Light feature in Excel.mp4 9.2 MB
- 3.2. K-Means/13.Interpreting a box plot.mp4 8.6 MB
- 3.2. K-Means/12.Which variables should be used with k-means.mp4 8.6 MB
- 7.6. Association Rules and Sequence Detection/35.Intro to association rules and sequence analysis.mp4 7.2 MB
- 5.4. Cluster Methods for Categorical Variables/26.Relating clusters to categories visually.mp4 6.6 MB
- 3.2. K-Means/11.How does k-means work.mp4 6.2 MB
- 1.Introduction/01.Welcome.mp4 6.1 MB
- 7.6. Association Rules and Sequence Detection/40.Comparing clustering and association rules.mp4 5.8 MB
- 7.6. Association Rules and Sequence Detection/37.Some association rules terminology.mp4 4.5 MB
- 1.Introduction/03.Using the exercise files.mp4 4.0 MB
- 1.Introduction/02.What you should know.mp4 3.3 MB
- 3.2. K-Means/16.What does silhouette mean.mp4 3.2 MB
- 8.Conclusion/42.Next steps.mp4 2.4 MB
- 5.4. Cluster Methods for Categorical Variables/29.Using cluster analysis and decision trees together.en.srt 14.8 KB
- 4.3. Visualizing and Reporting Cluster Solutions/24.Line graphs.en.srt 11.9 KB
- 7.6. Association Rules and Sequence Detection/38.Interpreting association rules.en.srt 11.2 KB
- 6.5. Anomaly Detection/32.The k = 1 trick.en.srt 11.1 KB
- 3.2. K-Means/13.Interpreting a box plot.en.srt 11.1 KB
- 5.4. Cluster Methods for Categorical Variables/31.A self organizing map example.en.srt 11.1 KB
- 5.4. Cluster Methods for Categorical Variables/25.Relating clusters to categories statistically.en.srt 10.8 KB
- 6.5. Anomaly Detection/34.Using SOM for anomaly detection.en.srt 9.9 KB
- 3.2. K-Means/19.Finding optimum value for k - k = 4.en.srt 9.6 KB
- 1.Introduction/04.What is unsupervised machine learning.en.srt 9.5 KB
- 3.2. K-Means/15.Interpreting cluster analysis output.en.srt 9.4 KB
- 2.1. What Is Cluster Analysis/05.Looking at the data with a 2D scatter plot.en.srt 9.4 KB
- 7.6. Association Rules and Sequence Detection/36.Running association rules.en.srt 9.3 KB
- 2.1. What Is Cluster Analysis/09.Methods for measuring distance.en.srt 9.3 KB
- 7.6. Association Rules and Sequence Detection/41.Sequence detection.en.srt 8.8 KB
- 5.4. Cluster Methods for Categorical Variables/27.Running a multiple correspondence analysis.en.srt 8.5 KB
- 2.1. What Is Cluster Analysis/10.What is k-nearest neighbors.en.srt 8.5 KB
- 2.1. What Is Cluster Analysis/06.Understanding hierarchical cluster analysis.en.srt 8.4 KB
- 3.2. K-Means/18.Finding optimum value for k - k = 3.en.srt 8.2 KB
- 4.3. Visualizing and Reporting Cluster Solutions/22.Summarizing cluster means in a table.en.srt 8.2 KB
- 7.6. Association Rules and Sequence Detection/35.Intro to association rules and sequence analysis.en.srt 7.9 KB
- 3.2. K-Means/20.Finding optimum value for k - k = 5.en.srt 7.8 KB
- 7.6. Association Rules and Sequence Detection/39.Putting association rules to use.en.srt 7.8 KB
- 3.2. K-Means/17.Which cases should be used with k-means.en.srt 7.6 KB
- 5.4. Cluster Methods for Categorical Variables/30.A BIRCH_two-step example.en.srt 7.5 KB
- 6.5. Anomaly Detection/33.Anomaly detection algorithms.en.srt 6.9 KB
- 2.1. What Is Cluster Analysis/07.Running hierarchical cluster analysis.en.srt 6.5 KB
- 2.1. What Is Cluster Analysis/08.Interpreting a dendrogram.en.srt 6.1 KB
- 4.3. Visualizing and Reporting Cluster Solutions/23.Traffic Light feature in Excel.en.srt 5.6 KB
- 3.2. K-Means/21.What the best solution.en.srt 5.5 KB
- 3.2. K-Means/14.Running a k-means cluster analysis.en.srt 5.3 KB
- 7.6. Association Rules and Sequence Detection/37.Some association rules terminology.en.srt 5.1 KB
- 3.2. K-Means/12.Which variables should be used with k-means.en.srt 5.1 KB
- 5.4. Cluster Methods for Categorical Variables/28.Interpreting a perceptual map.en.srt 5.1 KB
- 5.4. Cluster Methods for Categorical Variables/26.Relating clusters to categories visually.en.srt 4.9 KB
- 7.6. Association Rules and Sequence Detection/40.Comparing clustering and association rules.en.srt 4.5 KB
- 1.Introduction/02.What you should know.en.srt 3.9 KB
- 3.2. K-Means/16.What does silhouette mean.en.srt 3.6 KB
- 3.2. K-Means/11.How does k-means work.en.srt 3.2 KB
- 8.Conclusion/42.Next steps.en.srt 2.7 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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