This course covers data preparation for machine learning. The course includes example applications and example code. Best practices in telecommunication, data cleaning, data transformation, and how to turn the developed algorithms into a robust system are covered in depth. The benefits for taking this course include turning your users and system administrators into heroes, having a system that can remain viable, and successfully moving into the next great application while handing the system to lower level people.
What am I going to get from this course?
Select the appropriate robot for their application, integrate the real time data exchange at the appropriate frequency, include the predictors that may be needed for training, prepare the data for machine learning, and deploy robustly for success.
Prerequisites and Target Audience
What will students need to know or do before starting this course?
A knowledge of Python, C++, Kafka, et al, will help if the student really wants to be able to deploy, but these sections can be skipped for the functional user or executive who is interested at the survey level because the class is in the "for dummies" style.
Who should take this course? Who should not?
Because the course is in the "for dummies" style, anyone from functional staff to executives can take the course at the survey level. To really get to implement these topics, because the underlying math is not covered, a masters in quantitative methods is advised.
What this class is
This lecture differentiates between programming robots versus using fully functioning robots for applications.
Module 2: Real time data exchange
Robots with WiFi
Open source versus closed source robots with telecommunication is covered plus the current status of cellular on board.
AI applications for robots are discussed.
A robot for machine learning as a class example is selected.
Finding robots with WiFi or cellular on board.
Real Time Data Exchange
Robots with open source output files.
The current state of telecommunication on board robots.
TC Do's and Don't's
Best practices to avoid pitfalls.
Selection of an example for class.
Sample CSV file
Walkthrough of variables and format.
Sample variable list
This list is for the class example application.
Sample application 1
Frequency and variables for training the machine learning for application 1.
Sample application 1
Frequency and variables for training the machine learning for application 2.
Sample application 2
Submit the frequency and variables for training the machine learning for your application.
Obtaining the samples for training and later for the automated system.
Best practices and revisions and upgrades.
Pseudo real time collection
Real time to your application depends on the frequency of sampling.
The environment defined by your selected predictors may widen over time.
Results are only as good as the inputs and your understanding of the application.
Data usually needs to be transformed so that values which are too big or too small do not skew the results.
Building the algorithm
The numerous tools available to understand the interactions of the predictors.
How to construct the machine learning is covered in more depth in the next course the Industrial Internet of Things, AKA IIoT, AKA Machine to Machine, M2M.
Neural networks are covered in more depth in the next class, IIoT.
6 sigma alerts
SPC, AKA Statistical Process Control, is important for being able to manage when something in the predictors goes askew.
Logging sampling events is important when some predictor goes askew and needs investigation.
Being able to send logs and reports to the system user on demand or when a predictor goes askew via Android or iOS is key to keeping the system running 24/7.
Both user and system administrator documentation are important for successful deployment. This can be an AI project inside your machine learning application, LOL.
Best practices are suggested based on the number of locations in your installation and their geographic proximity to each other.
How to deploy and make your users and system administrators heroes, so that you can move onto your next exciting robot machine learning application.
A system that is not updated is not being used and will die from disuse.