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. 

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. 

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. 

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. 

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. 

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. 

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. 

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. 

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. 

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. 

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. 

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. 

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. 

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?

• 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?

• 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?

• 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?

• 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?

• 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?

• 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?

• 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?

• 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?

• 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?

• 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?

• 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?

• 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?

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

• Clustering with algorithms
• Regression models
• Classification models
• The most important metrics
• Practical examples in KNIME

• Clustering with algorithms
• Regression models
• Classification models
• The most important metrics
• Practical examples in KNIME

• Clustering with algorithms
• Regression models
• Classification models
• The most important metrics
• Practical examples in KNIME

• Clustering with algorithms
• Regression models
• Classification models
• The most important metrics
• Practical examples in KNIME

• Clustering with algorithms
• Regression models
• Classification models
• The most important metrics
• Practical examples in KNIME

• Clustering with algorithms
• Regression models
• Classification models
• The most important metrics
• Practical examples in KNIME

• Clustering with algorithms
• Regression models
• Classification models
• The most important metrics
• Practical examples in KNIME

• Clustering with algorithms
• Regression models
• Classification models
• The most important metrics
• Practical examples in KNIME

• Clustering with algorithms
• Regression models
• Classification models
• The most important metrics
• Practical examples in KNIME

• Clustering with algorithms
• Regression models
• Classification models
• The most important metrics
• Practical examples in KNIME

• Clustering with algorithms
• Regression models
• Classification models
• The most important metrics
• Practical examples in KNIME

• Clustering with algorithms
• Regression models
• Classification models
• The most important metrics
• Practical examples in KNIME

• 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

• How neural networks work
• Overview of the architectures
• Bringing machine learning into production - an outlook

• How neural networks work
• Overview of the architectures
• Bringing machine learning into production - an outlook

• How neural networks work
• Overview of the architectures
• Bringing machine learning into production - an outlook

• How neural networks work
• Overview of the architectures
• Bringing machine learning into production - an outlook

• How neural networks work
• Overview of the architectures
• Bringing machine learning into production - an outlook

• How neural networks work
• Overview of the architectures
• Bringing machine learning into production - an outlook

• How neural networks work
• Overview of the architectures
• Bringing machine learning into production - an outlook

• How neural networks work
• Overview of the architectures
• Bringing machine learning into production - an outlook

• How neural networks work
• Overview of the architectures
• Bringing machine learning into production - an outlook

• How neural networks work
• Overview of the architectures
• Bringing machine learning into production - an outlook

• How neural networks work
• Overview of the architectures
• Bringing machine learning into production - an outlook

• How neural networks work
• Overview of the architectures
• Bringing machine learning into production - an outlook

• 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. 

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. 

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. 

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. 

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. 

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. 

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. 

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. 

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. 

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. 

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. 

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. 

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. 

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.