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Machine Learning Master
eLearning
Overview
view on academy.rapidminer.comWhen machine learning and data science is addressed it is often overlooked that there is a great gap between producing a good model and having it run in production. No matter if it is used to do a scoring every week or trigger by an action in real-time, the challenges of monitoring, model deployment and management are always important to consider. With deployment often the visual representation of the result is a big topic, so we are addressing web apps here as well.
Introduction- Welcome
- Validation
- Performancet
- Thresholds
- Review and notes
- Feature Generation
- Feature Selection
- Evolutionary and other techniques
- Review and notes
- Parameter Optimization
- Review and Notes
Using R and Python
- Extension Configuration
- Integrating RapidMiner Studio and Python
- Integrating R & Python Models
- Review and notes
- Ensembles
- Support Vector Machines
- Deep Learning
- Review and notes
- Data Preparation and Analysis
- Time Series Forecasting and Modeling
- Review and notes
- Machine Learning Master