Description
Inspired by Albert Einstein [1879-1955]
Course summary:
Learn how to
identify anomaly
within several similar objects with
Artificial Intelligence
Working with
time-series sensor generated
data
Understand how
Unsupervised Machine Learning Algorithm
works using real life dataset
Learn developing in
R
and
ShinyApp
with a possibility to better explore the data, instantly deploy your project
Explained use of
Version Control
to be organized and save time
Practice 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 practice
Basic understanding on
Time-Series
data manipulation in R
More approaches of
Anomaly Detection
including
Deep Learning
on
h2o framework
is covered in the course
Practical Developing the idea of
Industrial
Process Control with Artificial Intelligence
with DEMO Shiny Application included
Course video captions are translated to [Chinese-Simplified, Hindi, German, French, Italian, Portuguese, Turkish, Spanish, Malay, Indonesian, Russian] languages
Described:
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
Requrirements
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