Identify Problems With Artificial Intelligence - Case Study

Industry 4.0: Bring your Complex Problem Solving Skills to a new level. Contains Deep Learning Tutorial.

What you'll learn
Identify anomaly within several similar objects
Apply Unsupervised Machine Learning algorithm kmeans
Develop and deploy ShinyApp
Apply Version Control to your projects or activities
Re-use provided template and course exercises in R and ShinyApp
Use Deep Learning Autoencoder Models to Detect Anomalies in Time-Series data
Create a System that Supervises Industrial Process and helps Process Operators to detect anomalies
Requirements
Computer with Internet connection
Mac or PC
R Statistical Software, R-Studio
Version Control Software e.g. Github for Desktop [recommended]
Installed Java on your computer
Description
Inspired by Albert Einstein [1879-1955]Course summary:Learn how to identify anomaly within several similar objects with Artificial IntelligenceWorking with time-series sensor generated dataUnderstand how Unsupervised Machine Learning Algorithm works using real life datasetLearn developing in R and ShinyApp with a possibility to better explore the data, instantly deploy your projectExplained use of Version Control to be organized and save timePractice with real life generalized Dataset coming from Manufacturing!Versatile method is presented using a Case Study approach.This method helped to discover real life inefficiency and to solve the problem!Start with R here! Step by step introduction with examples and practiceBasic understanding on Time-Series data manipulation in RMore approaches of Anomaly Detection including Deep Learning on h2o framework is covered in the coursePractical Developing the idea of Industrial Process Control with Artificial Intelligence with DEMO Shiny Application includedCourse video captions are translated to [Chinese-Simplified, Hindi, German, French, Italian, Portuguese, Turkish, Spanish, Malay, Indonesian, Russian] languagesDescribed:Problem-solving in Manufacturing is usually perceived as a slow and boring activity especially when many possible factors involved. At the same time it's often common that problems going on and on unobserved which is very costly. Is it possible to apply Artificial Intelligence to help human to identify the problem? Is it possible to dedicate this boring problem solving activity to computer? Apparently yes!!!This course will help you to combine popular problem-solving technique called "is/is not" with Artificial Intelligence in order to quickly identify the problem.We will use data coming from four similar Machines.  We will process it through the Unsupervised Machine Learning Algorithm k-means. Once you get intuition understanding how this system work You will be amazed to see how easy and versatile the concept is. In our project you will see that helped by Artificial Intelligence Human eye will easily spot the problem. Course will also exploit different other methods of Anomaly Detection. Probably the most interesting one is to use Deep Learning Autoencoders models built with help of H2O Platform in R.Using collected data and Expert Knowledge for Process Control with AI:In this course we will build and demo-try entire multi-variables process supervision system. Process Expert should select dataset coming from the ideally working process. Deep Learning model will be fit to that specific pattern. This model can be used to monitor the process as the new data is coming in. Anomaly in the process then can be easily detected by the process operators.Ready for Production:Another great value from the Course is the possibility to learn using ShinyApp. This tool will help you to instantly deploy your data project in no time!!! In fact all examples we will study will be ready to be deployed in real scenario!Additionally:You will learn R by practicing re-using provided material. More over you can easily retain and reuse the knowledge from the course - all lectures with code are available as downloadable html files.  You will get useful knowledge on Version Control to be super organized and productive.Finally:Join this course to know how to take advantage and use Artificial Intelligence in Problem Solving

Overview

Section 1: Goal of the Course

Lecture 1 Introduction to the course

Lecture 2 What we will use to learn

Lecture 3 Introducing our case study

Lecture 4 How to get the most of this course

Section 2: A bit of theory

Lecture 5 Ideas from Problem Solving

Lecture 6 What is k-means?

Section 3: A bit of practice

Lecture 7 Install R & R-Studio

Lecture 8 Practice Creating your Project and ShinyApp

Lecture 9 Get the code easy! A quick win!

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

Lecture 10 Introduction to the chapter…

Lecture 11 User Interface of ShinyApp - build HTML with R functions

Lecture 12 Server Part - Calling Data to ShinyApp

Lecture 13 Server Part - Manipulating Data in ShinyApp

Lecture 14 Using Interactive Inputs

Lecture 15 Unsupervised Machine Learning

Lecture 16 Creating Dynamic Outputs

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!!!

Lecture 20 Your Project - Solution, use and new challenge!!!

Section 6: Other Options, including Deep Learning

Lecture 21 Feature Engineering

Lecture 22 Wavelet Analysis

Lecture 23 Deep Learning Autoencoders in H20 - Install & Example

Lecture 24 Deep Learning with H2O - Build Model on our data

Lecture 25 Deep Learning with H2O - Use Model to predict

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

Lecture 28 Selecting the Dataset

Lecture 29 Fitting and testing the Model

Lecture 30 Demo ShinyApp

Lecture 31 Demo App in Action!

Section 8: Conclusion

Lecture 32 Where to learn more and stay curious?

Lecture 33 What have you learnt?

Lecture 34 Bonus Lecture Where to go from here?

Anyone willing to be more advanced in Complex Problem Solving,Production Supervisor or Process owner in Manufacturing,Data Analyst,Engineer