What you would learn in Identify Problems with Artificial Intelligence - Case Study course?
Solving problems in manufacturing is generally thought of as a tedious and monotonous task, especially when a lot of elements can be involved. It's typically the case that issues go around in silence, which can be very costly. Can we apply Artificial Intelligence to assist humans in determining the cause? Do you think it possible to assign this tedious problem-solving task to computers? Yes! This course will teach you to combine the favored problem-solving method known as "is/is not" with Artificial Intelligence to find the problem swiftly. The data we will use comes from four similar machines. We will process the data using our Unsupervised Machine Learning Algorithm k-means. Once you can clearly understand how this algorithm works, you will be amazed by how simple and flexible it is. In our work, you'll observe that the system is aided by artificial intelligence. Human eyes will be able to identify the issue quickly.
The course will also use different techniques of Anomaly detection. The most interesting is using Deep Learning Autoencoders models created with the help of the H2O Platform built-in R.
Utilizing expert knowledge and collected data to Control Control by AI:
In this class, we will create and demonstrate a complete multi-variables system for process supervision. Process Experts must select data from the optimal working process, and the Deep Learning model will be adjusted to this particular pattern. The model can keep track of the process as the new data comes in. The process managers can quickly identify any anomalies in the process.
ready for production:
Another benefit of the course is learning how to use ShinyApp. This tool will allow you to launch your data-related project in no time immediately!! In actuality, the examples will look at will be prepared to be used in a real-world scenario!
You'll learn the basics of R by reusing the provided materials. Additionally, you will be able to remember and reuse the lessons learned in the course. All lessons with code are accessible in pdf files. You will gain valuable knowledge regarding Version Control to become super-efficient and organized.
Find anomalies in the same objects
Use an Unsupervised Machine Learning algorithm to calculate k-means
Create and deploy ShinyApp
Use the Version Control for your activities or projects
Re-use templates and exercises for the course for course exercises in R and ShinyApp
Utilize Deep Learning Autoencoder Models to identify anomalies in Time-Series data
Create a System to supervise Industrial Process and helps Process Operators to identify irregularities
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