| 01 Apply logistic regressions to solve problems.en.srt | 1.8 KB | ||
| 01 Apply logistic regressions to solve problems.mp4 | 10.6 MB | ||
| 01_04_begin.xlsx | 9 KB | ||
| 01_04_end.xlsx | 9.2 KB | ||
| 02 What you should know.en.srt | 1.2 KB | ||
| 02 What you should know.mp4 | 6.2 MB | ||
| 03 Introduction to the course project.en.srt | 2.7 KB | ||
| 03 Introduction to the course project.mp4 | 15.2 MB | ||
| 04 Configuring the Excel Solver Add-in.en.srt | 2.7 KB | ||
| 04 Configuring the Excel Solver Add-in.mp4 | 16 MB | ||
| 05 Working with R.en.srt | 8.7 KB | ||
| 05 Working with R.mp4 | 50.6 MB | ||
| 06 Configuring R in Power BI.en.srt | 5.6 KB | ||
| 06 Configuring R in Power BI.mp4 | 31.9 MB | ||
| 07 Introducing AI and logistic regression.en.srt | 4 KB | ||
| 07 Introducing AI and logistic regression.mp4 | 21.4 MB | ||
| 08 Differentiating between odds and probabilities.en.srt | 3.9 KB | ||
| 08 Differentiating between odds and probabilities.mp4 | 21.8 MB | ||
| 09 Differentiating between distributions.en.srt | 3.3 KB | ||
| 09 Differentiating between distributions.mp4 | 17.7 MB | ||
| 10 Calculating logs and exponents.en.srt | 7.2 KB | ||
| 10 Calculating logs and exponents.mp4 | 43.5 MB | ||
| 11 Sigmoid curve.en.srt | 8.4 KB | ||
| 11 Sigmoid curve.mp4 | 51.9 MB | ||
| 12 Utilizing training and testing data sets.en.srt | 4.1 KB | ||
| 12 Utilizing training and testing data sets.mp4 | 23.4 MB | ||
| 13 Calculating linear regression.en.srt | 5.8 KB | ||
| 13 Calculating linear regression.mp4 | 31.7 MB | ||
| 14 Working with the logit model.en.srt | 6.7 KB | ||
| 14 Working with the logit model.mp4 | 41 MB | ||
| 15 Calculating log likelihood.en.srt | 7.7 KB | ||
| 15 Calculating log likelihood.mp4 | 44.7 MB | ||
| 16 Constructing MLE.en.srt | 13.1 KB | ||
| 16 Constructing MLE.mp4 | 91.2 MB | ||
| 17 Solving MLE.en.srt | 11.2 KB | ||
| 17 Solving MLE.mp4 | 68.3 MB | ||
| 18 Predicting outcomes.en.srt | 5.1 KB | ||
| 18 Predicting outcomes.mp4 | 33.8 MB | ||
| 19 Visualizing logistic regression.en.srt | 7.8 KB | ||
| 19 Visualizing logistic regression.mp4 | 50.2 MB | ||
| 20 Challenge Calculating logistic regression.en.srt | 1.4 KB | ||
| 20 Challenge Calculating logistic regression.mp4 | 7.3 MB | ||
| 21 Solution Calculating logistic regression.en.srt | 4.7 KB | ||
| 21 Solution Calculating logistic regression.mp4 | 28.5 MB | ||
| 22 Adding more independent variables.en.srt | 8.9 KB | ||
| 22 Adding more independent variables.mp4 | 57.9 MB | ||
| 23 Transforming variables.en.srt | 5.3 KB | ||
| 23 Transforming variables.mp4 | 34.1 MB | ||
| 24 Calculating correlations.en.srt | 9 KB | ||
| 24 Calculating correlations.mp4 | 57.7 MB | ||
| 25 Using statistics.en.srt | 5.6 KB | ||
| 25 Using statistics.mp4 | 38.1 MB | ||
| 26 Configuring confusion tables.en.srt | 16.2 KB | ||
| 26 Configuring confusion tables.mp4 | 110.5 MB | ||
| 27 Challenge Fine-tuning the model.en.srt | 1 KB | ||
| 27 Challenge Fine-tuning the model.mp4 | 6.3 MB | ||
| 28 Solution Fine-tuning the model.en.srt | 3.7 KB | ||
| 28 Solution Fine-tuning the model.mp4 | 25.1 MB | ||
| 29 Calculating odds for multinomial models.en.srt | 8.7 KB | ||
| 29 Calculating odds for multinomial models.mp4 | 57 MB | ||
| 30 Calculating probabilities for multinomial models.en.srt | 3 KB | ||
| 30 Calculating probabilities for multinomial models.mp4 | 20.3 MB | ||
| 31 Calculating multinomial log likelihoods.en.srt | 4.1 KB | ||
| 31 Calculating multinomial log likelihoods.mp4 | 25.4 MB | ||
| 32 Running MLE.en.srt | 6.1 KB | ||
| 32 Running MLE.mp4 | 39.9 MB | ||
| 33 Making the predictions.en.srt | 9.5 KB | ||
| 33 Making the predictions.mp4 | 65.2 MB | ||
| 34 Running R scripts in the Power Query Editor.en.srt | 9 KB | ||
| 34 Running R scripts in the Power Query Editor.mp4 | 62.8 MB | ||
| 35 Running R standard visuals.en.srt | 10.1 KB | ||
| 35 Running R standard visuals.mp4 | 66.9 MB | ||
| 36 Interacting between visual components.en.srt | 5.2 KB | ||
| 36 Interacting between visual components.mp4 | 33.4 MB | ||
| 37 Challenge Moving into Power BI.en.srt | 716.8 B | ||
| 37 Challenge Moving into Power BI.mp4 | 4.3 MB | ||
| 38 Solution Moving into Power BI.en.srt | 8.7 KB | ||
| 38 Solution Moving into Power BI.mp4 | 56.5 MB | ||
| 39 Next steps with logistic regressions.en.srt | 1.7 KB | ||
| 39 Next steps with logistic regressions.mp4 | 9.8 MB | ||
| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| binomial (testing).csv | 10.8 KB | ||
| binomial (training).csv | 42.4 KB | ||
| binomial.R | 614.4 B | ||
| binomial.pbix | 111.2 KB | ||
| binomial.xlsx | 675.6 KB | ||
| binomial_05_05_end.pbix | 149.7 KB | ||
| multinomial.R | 716.8 B | ||
| multinomial.xlsx | 350.9 KB | ||
| ▲ 155 total files | |||
Machine Learning with Logistic Regression in Excel, R, and Power BI
https://CourseMega.com
Duration: 2h 49m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 1.45 GB
Level: Intermediate | Genre: eLearning | Language: English
Excel, R, and Power BI are applications universally used in data science and across businesses and organizations around the world. If you’ve spent any time trying to figure out how to better model your data to get useful insights from it that you can act upon, you’ve most likely encountered these applications. In this course, Helen Wall shows how to use Excel, R, and Power BI for logistic regression in order to model data to predict the classification labels like detecting fraud or medical trial successes. Helen walks through several examples of logistic regression. She shows how to use Excel to tangibly calculate the regression model, then use R for more intensive calculations and visualizations. She then illustrates how to use Power BI to integrate the capabilities of Excel calculations and R in a scalable, sharable model.
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