Section 1 : Goal of the Course
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Lecture 1 | INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM | |
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Lecture 2 | What we will use to learn | 00:01:03 Duration |
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Lecture 3 | Introducing our case study | 00:05:35 Duration |
Section 2 : A bit of theory
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Lecture 1 | About Certification | |
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Lecture 2 | Ideas from Problem Solving | 00:04:01 Duration |
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Lecture 3 | What is k-means |
Section 3 : A bit of practice
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Lecture 1 | Install R & R-Studio | 00:04:19 Duration |
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Lecture 2 | Practice Creating your Project and ShinyApp | 00:09:56 Duration |
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Lecture 3 | Get the code easy! A quick win! | 00:06:30 Duration |
Section 4 : Let's Make it Happen or How our ShinyApp work!
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Lecture 1 | Introduction to the chapter | 00:08:47 Duration |
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Lecture 2 | User Interface of ShinyApp - build HTML with R functions | 00:07:39 Duration |
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Lecture 3 | Server Part - Calling Data to ShinyApp | 00:06:26 Duration |
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Lecture 4 | Server Part - Manipulating Data in ShinyApp | 00:05:21 Duration |
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Lecture 5 | Using Interactive Inputs | 00:08:38 Duration |
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Lecture 6 | Unsupervised Machine Learning | |
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Lecture 7 | Creating Dynamic Outputs | 00:08:27 Duration |
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Lecture 8 | Creating user preferred layout |
Section 5 : Your Project - New Data Set
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Lecture 1 | Your Project - Introducing New Dataset | |
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Lecture 2 | Your Project - apply method on other data!!! | 00:03:41 Duration |
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Lecture 3 | Your Project - Solution, use and new challenge!!! | 00:07:41 Duration |
Section 6 : Other Options, including Deep Learning
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Lecture 1 | Feature Engineering | 00:11:19 Duration |
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Lecture 2 | Wavelet Analysis | |
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Lecture 3 | Deep Learning Autoencoders in H20 - Install & Example | 00:09:34 Duration |
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Lecture 4 | Deep Learning with H2O - Build Model on our data | 00:08:49 Duration |
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Lecture 5 | Deep Learning with H2O - Use Model to predict | 00:10:16 Duration |
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Lecture 6 | Deep Learning with H2O - Put into production with ShinyApp |
Section 7 : Detect Anomaly in Industrial Process with Deep Learning
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Lecture 1 | Introducing the task and business need | 00:07:04 Duration |
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Lecture 2 | Selecting the Dataset | 00:07:52 Duration |
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Lecture 3 | Fitting and testing the Model | 00:09:17 Duration |
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Lecture 4 | Demo ShinyApp | 00:01:49 Duration |
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Lecture 5 | Demo App in Action! | 00:04:19 Duration |
Section 8 : Conclusion
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Lecture 1 | What have you learnt | |
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Lecture 2 | Bonus Lecture Where to go from here |
