Applied Machine Learning and Data Mining for Performance Analysis

This course teaches you how to use data mining and machine learning techniques to extract knowledge and insights from data. You will learn about the different methods and algorithms used in data mining and machine learning, as well as the ethical and regulatory considerations involved in using these techniques.

Applied Machine Learning and Data Mining for Performance Analysis covers the following topics:

• Statistical and probabilistic methods for data analysis
• Different methods for data mining
• Algorithms for supervised and unsupervised machine learning
• Neural networks and deep learning
• Data extraction and preparation
• Importing, combining, converting, and selecting data for learning and validation
• Validation methods and performance measures
• Visualization and analysis of results from data analysis
• Ethics and regulations concerning use and processing of personal data

By the end of the course, you will be able to:

• Apply methods to import, combine, and convert data into the appropriate format for data analysis
• Explain the benefits of data mining and choose and implement appropriate methods in typical data mining use cases
• Choose, motivate, and apply standard machine learning methods and algorithms to typical use cases and present the results in appropriate ways
• Design and perform performance validation of a machine learning system
• Give an account on ethics and regulations when using and processing personal data