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Course Outline
Introduction
- Overview of RapidMiner Studio
- Orientation to RapidMiner UI and features
CRISP-DM Methodology in RapidMiner
- Understanding CRISP-DM framework
- Application in estimation and projection of values
Data Understanding and Preparation
- Data import and exploration
- Preprocessing and cleaning techniques
- Advanced data transformation methods
Data Modeling with RapidMiner
- Introduction to data modeling
- Selection and application of machine learning algorithms
- Supervised learning algorithms
- Unsupervised learning algorithms
Model Evaluation and Deployment
- Techniques for model evaluation
- Strategies for model deployment
- Model realignment and optimization
Time Series Analysis and Forecasting
- Fundamentals of time series analysis
- Application of moving average models
- Time series preprocessing and data aggregation
Advanced Time Series Techniques
- Decomposition analysis
- Projection with time windows
- Projection with feature generation
ARIMA Modeling
- Understanding ARIMA models
- Practical application in RapidMiner
Summary and Next Steps
Requirements
- Basic understanding of data analysis and machine learning concepts
Audience
- Data Analysts
- Business Analysts
- Data Scientists
14 Hours
Testimonials (2)
Keeping it short and simple. Creating intuition and visual models around the concepts (decision tree graph, linear equations, calculating y_pred manually to prove how the model works).
Nicolae - DB Global Technology
Course - Machine Learning
It helped me achieve my goal of understanding ML. Much respect for Pablo for giving a proper introduction in this topic, since it becomes obvious after 3 days of training how vast this topic is. I have also enjoyed A LOT the idea of virtual machines you have provided, which had very good latency! It allowed every coursant to do experiments at their own pace.
