About this Training
The Machine Learning Specialist training introduces you to the world of AI through prominent examples and practical use cases in industry. In interactive sessions, you will discuss and develop an AI use case - from idea to implementation. In addition to data pipelines, data analysis processes and data mining tools, there will also be a special focus on data visualization and preparation. You will get to know different methods in the area of supervised learning, unsupervised learning, deep learning as well as reinforcement learning. Finally, you will get an insight into Big Data architectures and the Big Data ecosystem as well as an outlook on the topic of Edge AI.
Why this Training is right for you
The Machine Learning Specialist training is designed for technical professionals in the field of data analysis who want to understand the concrete technological methods and approaches and gain hands-on experience with the main methods of artificial intelligence. Basic programming skills are required.
Training Goals and Agenda
After this training, you will have gained an in-depth understanding of the different methods of artificial intelligence. Based on practical, interactive exercises, you will have learned, among other things, the most relevant machine learning techniques and gained a broad understanding of the individual AI building blocks.
Day 1 Welcome and Introduction to the Week & Introduction to Data Science and AI
- AI News and Applications
- AI Navigator (Use Case Browsing)
- Interactive session
- Data and Pipelines in an Industrial Context
- Processes of data analysis
- Data processing tools and visual analysis
- Discussion and Conclusion
Day 2 Introduction to Machine Learning & Exploratory Data Analysis
- Exploratory Data Analysis and Introduction to Visualization(Libriaries Part 1 and Part 2
- Theory and Examples: Supervised Learning
- Theory and Examples: Unsupervised Learning
- Discussion and Conclusion
Day 3 Practical Exercise
- Practical Exercise Part 1 Supervised Learning
- Practical Exercise Part 2: Supervised Learning
- Explainable AI
- Practical Exercise Part 3: Unsupervised Learning
- Practical Exercise Part 4: Unsupervised Learning
Day 4 Introduction to Deep Learning
- Practical Exercise Part 5: Deep Learning
- Practical Exercise Part 6: Deep Learning
- Introduction to Reinforcement Learning
- Discussion and Conclusion
Day 5 Introduction to Industrial Big Data & Exam
- Parallelize jobs with Map Reduce
- Big Data Ecosystems
- Interactive Session
- Outlook: Edge AI
- Feedback session
- Exam
- Final Discussion
- Awarding of Certificates
You will receive an officially recognized Professional Certificate "Machine Learning Specialist" from RWTH International Academy. The achievements can be credited to other certified courses or further studies, as they are marked with the "European Credit Transfer System" (ECTS) and contribute fundamentally to your professional development. In addition, you will receive the certificate as a verified digital badge protected and authenticated by the blockchain, which will specifically strengthen your professional portfolio through its unlimited visibility, security and flexibility.
Learn more about
- Building Blocks of AI
- Data Science
- Python for Data Science
- Databases & Pipelines
- Data Analysis & Visualization
- Explainable AI
- Machine Learning
- Supervised Learning
- Unsupervised Learning
- Deep Learning
INC Academy
academy@innovation-center.com
Our Partners
Speakers
Tim Schroeder studied Electrical Engineering & Information Technology as well as Business Administration and General Management at RWTH Aachen University. As the Head of Artificial Intelligence at INC Innovation Center, he conducted multiple bilateral and multilateral projects in the areas of industry 4.0, logistic 4.0, and artificial intelligence. Through technology scoutings, data analysis, and AI assessments, he helps companies to successfully implement new innovations.
Tim Schroeder
Dr. Christoph Rippe studied business mathematics with a focus on stochastics and optimization at Otto-von-Guericke University Magdeburg and then did his PhD in operations management on inventory management problems for service technicians. At INC Innovation Center, Christoph is a Senior Technology Specialist responsible for data analysis and prototypical implementation of AI use cases. He also supervises trainings and works on research topics related to AI applicability (small data augmentation/transfer learning).
Dr. Christoph Rippe
Since January 2020 Aymen Gannouni is a research associate at the chair of Information Management in Mechanical Engineering. He leads the research group Production Enhancing Technologies within the department Data Intelligence. Aymen Gannouni works on different digitalization projects of Industry 4.0 (e.g. construction sites, tunnel boring, textile manufacturing, plastic processing, battery cell manufacturing). Beforehand, he studied computer science with minor in business administration in Bachelors and Masters at the RWTH Aachen University.
Aymen Gannouni
Since February 2020 Hans Zhou works as a scientific associate at the Cybernetics Lab. There, his work revolves around research projects focusing on the application of machine learning for production and mechatronic systems. Previously, he studied mechanical and process engineering at the TU Darmstadt.
Hans Zhou
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Michael Zeng has been a research associate at the Cybernetics Lab at RWTH Aachen University since September 2019. As part of the research group Production-Enhancing Technologies, he works on the topics data lakes, production data management and cloud-edge data infrastructure. Michael Zeng studied physics and computational engineering at the universities of Marburg and Darmstadt.
Michael Zeng
Available Dates
Regular Price: 4,200€ (+ 19% VAT)
Regular Price: 4,200€ (+ 19% VAT)