MALDAS22 - MACHINE LEARNING AND DATA SCIENCE

Specific training objectives of the course are the following:
It distinguishes the characteristics and the different phases of the main Data Science project management models and their phases.
It distinguishes the different types of data in relation to the quantities to be represented and the operations supported
It composes basic workflows in KNIME, selecting and appropriately interconnecting the nodes.
It distinguishes the main data preparation operations and their purposes.
Compose data preparation workflows in KNIME using the appropriate nodes to manipulate and process the data
It distinguishes the characteristics and purposes of the different types of supervised and unsupervised Machine Learning problems.
It distinguishes the issues and stages of the evaluation process of a machine learning system.
Implement KNIME workflow to perform multiple regression analysis and result evaluation
It distinguishes the characteristics of the main machine learning approaches applied to Classification problems.
It defines the main metrics for evaluating a classification system.
Implement classification workflow in KNIME
It distinguishes the characteristics of the main machine learning approaches applied to Clustering problems.
Defines the main metrics for evaluating a clustering analysis.
Implement clustering workflow in KNIME