Section 1 : Goal of the Course

Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 2 What we will use to learn 1:3
Lecture 3 Introducing our case study 5:35

Section 2 : A bit of theory

Lecture 4 About Certification Pdf
Lecture 5 Ideas from Problem Solving 4:1
Lecture 6 What is k-means

Section 3 : A bit of practice

Lecture 7 Install R & R-Studio 4:19
Lecture 8 Practice Creating your Project and ShinyApp 9:56
Lecture 9 Get the code easy! A quick win! 6:30

Section 4 : Let's Make it Happen or How our ShinyApp work!

Lecture 10 Introduction to the chapter 8:47
Lecture 11 User Interface of ShinyApp - build HTML with R functions 7:39
Lecture 12 Server Part - Calling Data to ShinyApp 6:26
Lecture 13 Server Part - Manipulating Data in ShinyApp 5:21
Lecture 14 Using Interactive Inputs 8:38
Lecture 15 Unsupervised Machine Learning
Lecture 16 Creating Dynamic Outputs 8:27
Lecture 17 Creating user preferred layout

Section 5 : Your Project - New Data Set

Lecture 18 Your Project - Introducing New Dataset
Lecture 19 Your Project - apply method on other data!!! 3:41
Lecture 20 Your Project - Solution, use and new challenge!!! 7:41

Section 6 : Other Options, including Deep Learning

Lecture 21 Feature Engineering 11:19
Lecture 22 Wavelet Analysis Text
Lecture 23 Deep Learning Autoencoders in H20 - Install & Example 9:34
Lecture 24 Deep Learning with H2O - Build Model on our data 8:49
Lecture 25 Deep Learning with H2O - Use Model to predict 10:16
Lecture 26 Deep Learning with H2O - Put into production with ShinyApp

Section 7 : Detect Anomaly in Industrial Process with Deep Learning

Lecture 27 Introducing the task and business need 7:4
Lecture 28 Selecting the Dataset 7:52
Lecture 29 Fitting and testing the Model 9:17
Lecture 30 Demo ShinyApp 1:49
Lecture 31 Demo App in Action! 4:19

Section 8 : Conclusion

Lecture 32 What have you learnt Text
Lecture 33 Bonus Lecture Where to go from here Text