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
Testimonials (4)
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
The details and the presentation style.
Cristian Mititean - Accenture Industrial SS
Course - Azure Machine Learning (AML)
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete