Data Analytics and Machine Learning in Production in Suzhou
苏州市 生产领域中的数据分析和机器学习

Target Group

This training was developed for engineers who want to develop their data understanding and drive digitalization.

Objectives and Content

The ubiquitous use of sensors and connected devices leads to a tremendous increase in collected data. Data analytics in production is the use of big data analytics and machine learning methods to ensure quality, to increase performance, and to reduce cost. This training combines theoretical knowledge and hands-on practices to show the potential of data analytics in production. Participating in this training helps you to understand and drive integration of appropriate methods on your way to a smart factory.

Why this training is very important for your career and for your company?

• Learn how to increase productivity by choosing the right tasks for lean production
• learn how to increase the profit and growth on your shop floor using lean methods and tools
• Get an overview of benefits from Data Analytics and Machine Learning in Production

Outline

• Introduction of Data Analytics & Machine Learning
  • General Introduction
  • Standard CRISP-DM process model CRISP-DM
  • Examples for analysis & implementation
• Machine Learning Algorithms
  • Supervised learning (e.g., Artificial Neural Networks)
  • Unsupervised learning (e.g., Support Vector Machines)
  • Reinforcement learning (e.g., Q-Learning)
• Data analysis platform & tools
  • Knime
  • Python
  • TensorFlow and Keras
• Introduction of Industry implementation examples
• Guided practice on chosen use cases

参加对象

本次培训为想要拓展对数据的理解并推动数字化发展的工程师开发。

目标与内容

传感器和连接设备的普遍使用导致收集的数据量大幅增加。生产中的数据分析使用大数据分析与机器学习方法来确保质量、提高性能并降低成本。本次培训结合了理论知识和动手实践,展示数据分析在生产中的潜力。参加此培训可帮助您在通往智能工厂的道路上理解并整合适当的方法。

大纲

• 数据分析 / 机器学习介绍
  • 基本概念介绍
  • 数据挖掘标准流程
  • 案例分析和演练

• 机器学习算
  • 监督学习(例如人工神经网络)
  • 无监督学习(例如支持向量机)
  • 强化学习(例如 Q-学习)

• 数据分析平台&工具应用
  • 案例
  • 案例
  • 案例
• 工业应用案例介绍
• 针对所选案例的指导性实践