| 01 - AI and machine learning.mp4 | 16.4 MB | ||
| 01 - AI and machine learning.srt | 11.7 KB | ||
| 01 - Analyzing potential model dimensions.mp4 | 30 MB | ||
| 01 - Analyzing potential model dimensions.srt | 10.8 KB | ||
| 01 - Calculating distances.mp4 | 28.5 MB | ||
| 01 - Calculating distances.srt | 11.5 KB | ||
| 01 - Next steps.mp4 | 2.7 MB | ||
| 01 - Next steps.srt | 2.3 KB | ||
| 01 - Setting up R in Power Query Editor.mp4 | 16 MB | ||
| 01 - Setting up R in Power Query Editor.srt | 7.6 KB | ||
| 01 - Use data reduction for valuable insights.mp4 | 6 MB | ||
| 01 - Use data reduction for valuable insights.mp4.jpg?042148 | 174.9 KB | ||
| 01 - Use data reduction for valuable insights.srt | 2 KB | ||
| 01 - Visualizing PCA.mp4 | 34.8 MB | ||
| 01 - Visualizing PCA.srt | 12.9 KB | ||
| 02 - Creating custom code with R standard visual.mp4 | 14.3 MB | ||
| 02 - Creating custom code with R standard visual.srt | 5.9 KB | ||
| 02 - Hierarchical clustering.mp4 | 52.2 MB | ||
| 02 - Hierarchical clustering.srt | 12 KB | ||
| 02 - Numerosity.mp4 | 23.8 MB | ||
| 02 - Numerosity.srt | 11 KB | ||
| 02 - Removing or replacing null values.mp4 | 15.4 MB | ||
| 02 - Removing or replacing null values.srt | 5.8 KB | ||
| 02 - Using Excel Solver to find solutions.mp4 | 18.3 MB | ||
| 02 - Using Excel Solver to find solutions.srt | 8 KB | ||
| 02 - What you should know.mp4 | 1.5 MB | ||
| 02 - What you should know.srt | 1.4 KB | ||
| 03 - Challenge Power BI.mp4 | 1.4 MB | ||
| 03 - Challenge Power BI.srt | 614.4 B | ||
| 03 - Dimensionality.mp4 | 17.8 MB | ||
| 03 - Dimensionality.srt | 9.1 KB | ||
| 03 - Heatmaps and dendrograms.mp4 | 20.7 MB | ||
| 03 - Heatmaps and dendrograms.srt | 8.2 KB | ||
| 03 - Introducing the course project.mp4 | 9.8 MB | ||
| 03 - Introducing the course project.srt | 4 KB | ||
| 03 - Solving for principal components axes.mp4 | 18.4 MB | ||
| 03 - Solving for principal components axes.srt | 3.9 KB | ||
| 04 - Aggregating or grouping data.mp4 | 34.7 MB | ||
| 04 - Aggregating or grouping data.srt | 12.9 KB | ||
| 04 - Configuring Excel Solver Add-in.mp4 | 4.3 MB | ||
| 04 - Configuring Excel Solver Add-in.srt | 3 KB | ||
| 04 - Eigenvalues.mp4 | 16.1 MB | ||
| 04 - Eigenvalues.srt | 8.3 KB | ||
| 04 - K-means clustering in one dimension.mp4 | 40.7 MB | ||
| 04 - K-means clustering in one dimension.srt | 12.8 KB | ||
| 04 - Solution Power BI.mp4 | 23.4 MB | ||
| 04 - Solution Power BI.srt | 9.3 KB | ||
| 05 - Eigenvectors.mp4 | 27.9 MB | ||
| 05 - Eigenvectors.srt | 13.8 KB | ||
| 05 - Histograms.mp4 | 19.8 MB | ||
| 05 - Histograms.srt | 7.9 KB | ||
| 05 - K-means clustering in two dimensions.mp4 | 17.1 MB | ||
| 05 - K-means clustering in two dimensions.srt | 7.3 KB | ||
| 05 - Working with R.mp4 | 18.3 MB | ||
| 05 - Working with R.srt | 10.2 KB | ||
| 06 - Binning.mp4 | 14 MB | ||
| 06 - Binning.srt | 7.9 KB | ||
| 06 - Configuring R in Power BI.mp4 | 11.8 MB | ||
| 06 - Configuring R in Power BI.srt | 5.8 KB | ||
| 06 - Determining k.mp4 | 28.2 MB | ||
| 06 - Determining k.srt | 11.7 KB | ||
| 06 - PCA projection space.mp4 | 24.3 MB | ||
| 06 - PCA projection space.srt | 8.6 KB | ||
| 07 - Challenge Clustering.mp4 | 2 MB | ||
| 07 - Challenge Clustering.srt | 1.2 KB | ||
| 07 - Correlation and covariance.mp4 | 31.6 MB | ||
| 07 - Correlation and covariance.srt | 13.9 KB | ||
| 07 - Scree plot.mp4 | 6.8 MB | ||
| 07 - Scree plot.srt | 3.5 KB | ||
| 08 - Challenge Getting the data.mp4 | 3.7 MB | ||
| 08 - Challenge Getting the data.srt | 2 KB | ||
| 08 - Challenge PCA.mp4 | 2.1 MB | ||
| 08 - Challenge PCA.srt | 1.2 KB | ||
| 08 - Solution Clustering.mp4 | 28.5 MB | ||
| 08 - Solution Clustering.srt | 10.9 KB | ||
| 09 - Solution Getting the data.mp4 | 7.2 MB | ||
| 09 - Solution Getting the data.srt | 3.1 KB | ||
| 09 - Solution PCA.mp4 | 14.1 MB | ||
| 09 - Solution PCA.srt | 5.8 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Challenge.R | 512 B | ||
| Correlations and Covariances.R | 512 B | ||
| Correlations and Covariances.xlsx | 42.5 KB | ||
| Data Reduction.pbix | 152.5 KB | ||
| GHCND_documentation (from NOAA).pdf | 97.5 KB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| Grouping.R | 716.8 B | ||
| Grouping.xlsx | 75.4 MB | ||
| Hierarchical Clustering.R | 512 B | ||
| Hierarchical Clustering.xlsx | 2.2 MB | ||
| Histogram.R | 512 B | ||
| Histogram.xlsx | 75.6 MB | ||
| KMeans (updated setseed from setseeds).R | 614.4 B | ||
| KMeans.R | 614.4 B | ||
| KMeans.xlsx | 48.9 KB | ||
| Missing Values.R | 409.6 B | ||
| Missing Values.xlsx | 30.2 KB | ||
| Model Variables.xlsx | 31.4 KB | ||
| NOAA data (Top 25 metro areas).csv | 130.9 MB | ||
| Numerosity and Dimensionality.R | 307.2 B | ||
| Numerosity and Dimensionality.xlsx | 55.7 MB | ||
| PCA.R | 614.4 B | ||
| PCA.xlsx | 50 KB | ||
| ▲ 176 total files | |||
Machine Learning with Data Reduction in Excel, R, and Power BI
https://CoursePig.com
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 3h 32m | Size: 1.32 GB
Analytics is a big part of how the world does data science. But did you know that you can use applications like Excel, R, and Power BI for high-dimensional data reduction with machine learning models and algorithms? In this course, instructor Helen Wall gives you an overview of machine learning and data reduction techniques that enable you to analyze large datasets and determine trends with a variety of different classifications.
Learn about machine learning models like clusters and anomaly detection algorithms. Find out more about distance, dimensionality, and granularity, as you explore dimensional and numerical data reduction techniques, analytic models, and visualization tools in Excel, R, and Power BI. Along the way, get tips on how to integrate your methods so you can scale them for sharing with a wider audience.
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