The Learning objective is to understand the impact of Machine Learning on all aspects of life and the opportunities as well as risks associated with the digital disruption. The course covers the management perspective (concepts and end-user perspective) as well as the IT perspective (Implementation of Machine Learning solutions).

The topics covered include:

• Use cases of Machine Learning
• Business value
• Architectures and tools for Machine Learning
• Data Science Lifecycle
• Discovery
• Data preparation (Extraction, transformation, load)
• Model planning
• Model building & evaluation
• Communication
• Analytical methods and models for machine learning
• Exploratory data analysis (descriptive, predictive)
• Clustering
• Association rules
• Regression
• Classification
• Time series analysis & Forecasting
• Text analysis
• Unstructured Data analysis
• Data visualization

The exercises are performed using a selection of tools from the following list: Python, Jupyter, R, RStudio using the libraries of Scikit-Learn, TensorFlow, Keras and Tidymodels.