Did you know?
This course is part of the certified Master Class "KI Manager:in" and the Master Class "Machine Learning Engineer". If you book the entire Master Class, you save over 20% compared to booking the individual modules.
After the introduction and an introduction to the learning environment, we get straight into the topic: Your:e trainer will go through a first complete pipeline with you, from data preparation to training the data model.
- Basics of data mining and machine learning
- Important learning procedures explained in detail
- Implementing data projects with CRISP-DM
- Business Understanding: What does data say?
- All about data types and data sources
- Understanding data: What information does data contain?
- First practical examples with exercises in KNIME
- Data preparation for data projects
After reflecting together on the content from the first self-study phases, your:e trainer will introduce you to practical work with the most important data tools. This will give you the knowledge you need to complete your own projects.
- Clustering with algorithms
- Regression models
- Classification models
- The most important metrics
- Practical examples in KNIME
- How neural networks work
- Overview of the architectures
- Bringing machine learning into production - an outlook
Once all the questions from the self-study units have been answered, the participants' practical exercise projects are evaluated. Your:e trainer will then give you an outlook on the deployment of machine learning models.
Contents and course schedule
1. machine learning and data mining
- Basics of data mining and machine learning
- Important learning procedures explained in detail
- Implementing data projects with CRISP-DM
- Business Understanding: What does data say?
2. data understanding and data preparation
- All about data types and data sources
- Understanding data: What information does data contain?
- First practical examples with exercises in KNIME
- Data preparation for data projects
3. modeling and evaluation
- Clustering with algorithms
- Regression models
- Classification models
- The most important metrics
- Practical examples in KNIME
4. an outlook on deep learning in neural networks
- How neural networks work
- Overview of the architectures
- Bringing machine learning into production - an outlook
- Excursus: Deep learning as a sub-area of machine learning
This is how you learn in this course
This online course offers you a digital blended concept that has been developed for part-time learning. With a time budget of at least 3-4 hours per week, you are sure to reach your goal. Alternatively, you can schedule the learning units flexibly. This is how you learn in the course:
Self-study phases: Learn independently, at your own pace and whenever you want. Our courses offer you didactically high-quality learning material with videos, articles, interactive exercises, quizzes and learning checks.
Live webinars: In regular online seminars, you will meet your trainers in person. You will receive answers to your questions, specific assistance and instructions on how to deepen your knowledge and apply the skills you have acquired in practical exercises.
Practical tasks: In order to learn the skills in practice, you will be given access to training sets that you can work on yourself using Python or the open software KNIME. You will clean up data, train your own models and get to know important tools. No prior knowledge is required.
Learning community: A digital learning community is available to you throughout the course. Exchange ideas with other participants and the trainers and clarify your questions.
Future Jobs Club: Get exclusive access to a business network, micro-learningssparks), news and future work hacks.
Certificate of attendance and Open Badge: As a graduate of the course, you will receive a certificate and an Open Badge that you can easily share in professional networks (e.g. LinkedIn).
Your benefit
You will learn what artificial intelligence is, how it works and what you can use AI for.
You will learn about the importance of data for automation, analysis and the creation of models and what is important when it comes to data quality .
You can clean and prepare data to implement your own mining or machine learning projects.
You will get to know the technical processes in machine learning and will be able to communicate these clearly within the company.
You will gain your first practical experience of working on data and training sets and will be able to apply your knowledge immediately afterwards.
You are able to make informed decisions about the use of AI in your company and can communicate with technical departments on an equal footing.
You will qualify in a new field of expertise that will play a major role in the future and is already in high demand today.
Take an active part in our online community and work with your own questions - this is how you will benefit most from this online training. This will allow you to apply the content both in self-study and in practical exercises.
Methods
Well-founded trainer, presentations, practical exercises, self-reflection, discussions, work aids, group work on participants' real projects and exchange of experience in the learning community.
- KNIME
Recommended for
This course is aimed at anyone who wants to know exactly what AI is all about. We recommend this course to decision makers in companies as well as product managers, product owners and process managers who want to automate processes with AI. The course is also an ideal introduction to data mining and AI for technical managers and specialists.
Overall, the course is the ideal introduction to AI and machine learning. It is equally suitable for newcomers and career changers as well as for people with prior knowledge who want to deepen their AI knowledge and put it into practice.
Attendees comments
"Despite the complexity of the subject, everything was explained in an understandable and practical way. The lessons and topics were structured and the documents for self-study were great. The practical work was fun and the lecturer was very competent!"

"The topic is challenging and complex. I was amazed at how the trainer managed to convey the content so clearly and methodically. Really great!"

Seminar evaluation for "Machine learning and data mining: concepts, models, learning methods"







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