Python for Industry 4.0

Posted in: Tutorials | By: AD-TEAMSS | 6-08-2022, 22:20 | 0 Comments
06
August
2022
Python for Industry 4.0
Published 08/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 41 lectures (4h 1m) | Size: 1.43 GB


Learn the basics of Python for Industry 4.0 with hands-on exercises and projects


What you'll learn
Load, work, and visualize real manufacturing data
Basic Python programming with Numpy, Pandas, Matplotlib, and other libraries
Complete a project on calculate Overall Equipment Effectiveness of two production lines
Complete a project on Control Loop Process Monitoring based on real dataset from a refinary

Requirements
No programming experience needed

Description
Industry 4.0 is a concept that promises huge performance increases for manufacturing. Gradually, companies are starting to implement its concepts and are looking for professionals with experience in the area. This course is the first step for future studies and development on different technologies of Industry 4.0, such as Machine Learning, Internet of Things, and real-time optimization, for example.
The course is divided into chapters, which end with exercises for you to test and improve your Python programming skills. We will be using Google Colab for our classes. So no setup is required. You will be able to start coding as soon as we get started with the practical classes.
First, we are going to cover the basic operations and syntax and also the main Python numerical and visualization libraries. This knowledge will be used in practical exercises for loading a dataset from a steel industry and building algorithms for data resampling and aggregation, data compression, filtering noise, identifying outliers, and others.
At the end of the course, you will work on two projects. The first one is the development of an algorithm to calculate the Overall Equipment Effectiveness (or OEE) of two production lines. The second one is the development of control loop performance indicators based on a real refinery dataset.

Who this course is for
Individuals looking for first python-based application in manufacturing


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