| APU000072610.csv | 8.5 KB | ||
| ARIMA and TBATS Handout.pdf | 30.3 KB | ||
| Bonus Resources.txt | 307.2 B | ||
| Challenge.pbix | 207 KB | ||
| Electricity Demand.csv | 297.5 MB | ||
| Electricity Generated.csv | 456.7 MB | ||
| Electricity Outages.csv | 258.3 KB | ||
| Energy Analysis.pbix | 19.4 MB | ||
| Error Logs.pbix | 322.9 KB | ||
| Error Logs.xlsx | 12.5 KB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| Installing R Documentation.pdf | 29.8 KB | ||
| KinetEco URLs.pbix | 13.9 KB | ||
| KinetEco sun and windmill logo on transparent background_LIL_11650.png | 62.4 KB | ||
| Linear Regression Calculations.xlsx | 114 KB | ||
| PCETRIM12M159SFRBDAL.csv | 8.1 KB | ||
| Power BI Query Keys.xlsx | 22.5 KB | ||
| Quick Insights Dataset.pbix | 8.2 MB | ||
| Scatter Plot Data Points.xlsx | 28.1 KB | ||
| Temperature Future.csv | 43.2 KB | ||
| Temperatures.csv | 13.3 MB | ||
| [10] Challenge Multiple variables.mp4 | 1.5 MB | ||
| [10] Challenge Multiple variables.srt | 1.4 KB | ||
| [10] Solution Single variable.mp4 | 10.7 MB | ||
| [10] Solution Single variable.srt | 4.3 KB | ||
| [11] Solution Multiple variables.mp4 | 13.8 MB | ||
| [11] Solution Multiple variables.srt | 5.6 KB | ||
| [1] Configurating aggregations and dimensionality.mp4 | 16.1 MB | ||
| [1] Configurating aggregations and dimensionality.srt | 7.7 KB | ||
| [1] Designing a consolidated view for sharing.mp4 | 17 MB | ||
| [1] Designing a consolidated view for sharing.srt | 9.2 KB | ||
| [1] Determining key drivers with decomposition tree visual.mp4 | 11.9 MB | ||
| [1] Determining key drivers with decomposition tree visual.srt | 6.3 KB | ||
| [1] How to learn ML and AI in Power BI.mp4 | 1.8 MB | ||
| [1] How to learn ML and AI in Power BI.srt | 819.2 B | ||
| [1] Organizing time series analysis.mp4 | 19.2 MB | ||
| [1] Organizing time series analysis.srt | 8.4 KB | ||
| [1] The power of Power BI.mp4 | 4.6 MB | ||
| [1] The power of Power BI.srt | 1.8 KB | ||
| [1] Utilizing AI in the ETL framework.mp4 | 15.7 MB | ||
| [1] Utilizing AI in the ETL framework.srt | 7.6 KB | ||
| [1] Visualizing relationships with scatter plots.mp4 | 16.4 MB | ||
| [1] Visualizing relationships with scatter plots.srt | 7.4 KB | ||
| [2] Accessing the Analytics pane.mp4 | 12.8 MB | ||
| [2] Accessing the Analytics pane.srt | 6.5 KB | ||
| [2] Adding forecasting from the Analytics pane.mp4 | 6.2 MB | ||
| [2] Adding forecasting from the Analytics pane.srt | 3.3 KB | ||
| [2] Configuring parameters.mp4 | 17.5 MB | ||
| [2] Configuring parameters.srt | 8.5 KB | ||
| [2] Filtering options.mp4 | 19.4 MB | ||
| [2] Filtering options.srt | 8.1 KB | ||
| [2] Leveraging the Q&A visual.mp4 | 15.1 MB | ||
| [2] Leveraging the Q&A visual.srt | 8.9 KB | ||
| [2] Uploading and sharing in the Power BI service.mp4 | 10.7 MB | ||
| [2] Uploading and sharing in the Power BI service.srt | 7.3 KB | ||
| [2] What you should know.mp4 | 2.5 MB | ||
| [2] What you should know.srt | 1.7 KB | ||
| [3] Analyzing dataset statistics and distributions.mp4 | 12.3 MB | ||
| [3] Analyzing dataset statistics and distributions.srt | 6.3 KB | ||
| [3] Calculating DAX measures.mp4 | 19.4 MB | ||
| [3] Calculating DAX measures.srt | 8.7 KB | ||
| [3] Calculating correlations.mp4 | 19.6 MB | ||
| [3] Calculating correlations.srt | 7.7 KB | ||
| [3] Configuring quick insights.mp4 | 10.3 MB | ||
| [3] Configuring quick insights.srt | 6.8 KB | ||
| [3] Discovering key insights with the Key Influencer visual.mp4 | 9.6 MB | ||
| [3] Discovering key insights with the Key Influencer visual.srt | 3.5 KB | ||
| [3] Leveraging anomaly detection.mp4 | 23 MB | ||
| [3] Leveraging anomaly detection.srt | 8.7 KB | ||
| [3] Overviewing AI and machine learning types.mp4 | 6.3 MB | ||
| [3] Overviewing AI and machine learning types.srt | 5.4 KB | ||
| [4] Challenge Shared view.mp4 | 2.7 MB | ||
| [4] Challenge Shared view.srt | 2.4 KB | ||
| [4] Challenge Single variable.mp4 | 4.5 MB | ||
| [4] Challenge Single variable.srt | 2.5 KB | ||
| [4] Configuring separate error logs for existing datasets.mp4 | 18.8 MB | ||
| [4] Configuring separate error logs for existing datasets.srt | 9.1 KB | ||
| [4] Defining dimensionality.mp4 | 3.3 MB | ||
| [4] Defining dimensionality.srt | 3.3 KB | ||
| [4] Utilizing ARIMA forecasting.mp4 | 10.1 MB | ||
| [4] Utilizing ARIMA forecasting.srt | 4.8 KB | ||
| [4] Utilizing parameters to model what-if scenarios.mp4 | 17.4 MB | ||
| [4] Utilizing parameters to model what-if scenarios.srt | 8.9 KB | ||
| [4] Visualizing correlations.mp4 | 21.7 MB | ||
| [4] Visualizing correlations.srt | 8.4 KB | ||
| [5] Adding clustering to existing visuals.mp4 | 13.7 MB | ||
| [5] Adding clustering to existing visuals.srt | 5.9 KB | ||
| [5] Calculating rolling averages.mp4 | 20.7 MB | ||
| [5] Calculating rolling averages.srt | 10.3 KB | ||
| [5] Challenge AI visuals.mp4 | 1.3 MB | ||
| [5] Challenge AI visuals.srt | 1.2 KB | ||
| [5] Incorporating seasonality through TBATS forecasting.mp4 | 15.6 MB | ||
| [5] Incorporating seasonality through TBATS forecasting.srt | 6.6 KB | ||
| [5] Running Vision algorithms.mp4 | 13.2 MB | ||
| [5] Running Vision algorithms.srt | 7.3 KB | ||
| [5] Solution Shared view.mp4 | 9 MB | ||
| [5] Solution Shared view.srt | 4.5 KB | ||
| [5] Utilizing the Power BI ecosystem and Azure.mp4 | 4.7 MB | ||
| [5] Utilizing the Power BI ecosystem and Azure.srt | 4.9 KB | ||
| [6] Analyzing predictions vs. actuals.mp4 | 24.2 MB | ||
| [6] Analyzing predictions vs. actuals.srt | 10.2 KB | ||
| [6] Calculating best fit line.mp4 | 31.7 MB | ||
| [6] Calculating best fit line.srt | 8.9 KB | ||
| [6] Configuring R in Power BI Desktop.mp4 | 8.2 MB | ||
| [6] Configuring R in Power BI Desktop.srt | 4.9 KB | ||
| [6] Solution AI visuals.mp4 | 11.1 MB | ||
| [6] Solution AI visuals.srt | 5 KB | ||
| [6] Utilizing Text Analytics algorithms.mp4 | 11.4 MB | ||
| [6] Utilizing Text Analytics algorithms.srt | 5.3 KB | ||
| [6] Utilizing binning to create histograms.mp4 | 15.2 MB | ||
| [6] Utilizing binning to create histograms.srt | 8 KB | ||
| [7] Challenge Time series analysis.mp4 | 1.8 MB | ||
| [7] Challenge Time series analysis.srt | 1.7 KB | ||
| [7] Introducing the course project.mp4 | 7.9 MB | ||
| [7] Introducing the course project.srt | 4.6 KB | ||
| [7] Leveraging AI and the star schema.mp4 | 18.8 MB | ||
| [7] Leveraging AI and the star schema.srt | 10.1 KB | ||
| [7] Summarizing statistics.mp4 | 15.3 MB | ||
| [7] Summarizing statistics.srt | 6.4 KB | ||
| [7] Utilizing the outlier detection visual.mp4 | 13.5 MB | ||
| [7] Utilizing the outlier detection visual.srt | 6.8 KB | ||
| [8] Adjusting DateTime fields for lags.mp4 | 16.1 MB | ||
| [8] Adjusting DateTime fields for lags.srt | 6.4 KB | ||
| [8] Calculating outliers.mp4 | 12.1 MB | ||
| [8] Calculating outliers.srt | 5.9 KB | ||
| [8] Solution Time series analysis.mp4 | 4.7 MB | ||
| [8] Solution Time series analysis.srt | 2.3 KB | ||
| [8] Splitting a category with small multiples.mp4 | 7.7 MB | ||
| [8] Splitting a category with small multiples.srt | 3.5 KB | ||
| [9] Contextualizing outliers.mp4 | 8.3 MB | ||
| [9] Contextualizing outliers.srt | 2.9 KB | ||
| [9] Leveraging violin plots.mp4 | 12.8 MB | ||
| [9] Leveraging violin plots.srt | 6.4 KB | ||
| ▲ 211 total files | |||
Power BI: Integrating AI and Machine Learning
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 3h 58m | Size: 1.82 GB
Power BI is a powerful data analytics and visualization tool that allows business users to monitor data, analyze trends, and make decisions. Every month, Microsoft pushes Power BI updates out to the end users, and the growth of Power BI is a key part of Microsoft’s current strategy. This course showcases existing AI and machine learning capabilities available directly in accessible Power BI functionalities. Data analytics and business analysis expert Helen Wall gives you a useful overview of Power BI, then dives into the steps to configure Power Query and your data model. Helen steps through analyzing single variables and shows you the tools and techniques to measure relationships between variables. She shows you visuals that you can use to pose and answer questions in Power BI, explains useful techniques to enhance your analysis of time series data, and walks you through some best practices for sharing your analysis
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