Course Overview
Type of course: classroom training, duration: 2 days
Basics 2 is a two-day training course focusing on advanced technologies and applications from the field of artificial intelligence and predictive modelling. The RapidMiner platform is used in the training course and requires no prior programming knowledge. During the two days, profiling in particular is looked at in depth. Participants will learn how existing data stocks can be prepared in order to optimally represent the information contained therein for the creation of predictive models.
In addition, further complex learning approaches such as neural networks and support vector machines are introduced and practically applied. In doing so, technologies are taught which can be combined with the algorithms in order to optimize forecasting quality. This includes both optimizing the learning algorithm itself via its setting parameters and selecting the useful influencing variables.
After the training course, participants will be able to prepare data for analysis, build and validate predictive models and apply models in simple contexts. Completing the course also enables you to attend the advanced training courses Deployment - Predictive Analytics in Live Use and Predictive Analytics in a Big Data Context.
Course Objectives
The skills acquired by participants of the training course include:
- Advanced methods of data preparation
- Creating complex analytical predictive models
- Using analytical predictive models
- Evaluating and optimizing models in relation to different quality criteria
Training Content - Basics 2
Overview
- Business scenario
- Recap of introductory course
- Loading new data
Exploratory Data Analysis
- Multiple data sources
- Joins & set theory
- Understanding new attributes
Preparing Data
- Advanced methods for data ETL (Extract, Transform, Load)
- Aggregation & multi-level aggregation
- Pivot & de-pivot
- Calculated values
- Regular expressions (Regex)
- Changing value types
- Feature generation and processing
- Loops
- Macros
Predictive Modelling Algorithms
- Support vector machines (SVM)
- Neural networks
- Logistic regression
Creating and Evaluating Models
- Advanced quality criteria
- ROC plots
- Comparing models
- Sampling
- Weighting
- Feature selection: Forward selection
- Feature selection: Backward elimination
- Validation of preprocessing and preprocessing models
- Logging optimization of parameters & results
Additional Workshops
- Principal component analysis
- Logistic regression
- Cost matrix and model optimization
Previous Knowledge Required
To attend Basics 2, you need prior knowledge from the training course Basics 1. If you already have extensive prior knowledge and do not wish to attend the Basics 1 training course, please contact us.
Target Group
Aspiring data scientists, engineers, experts and analysts
The training courses are specifically aimed at all those wishing to get involved with data science. They teach basic knowledge that is necessary to successfully realize and manage data science projects in the area of artificial intelligence. As such, they are directed at engineers, business, financial and technology analysts and other experts. The courses are also suitable for training new data scientists or experienced data scientists in order to ease their transition to the RapidMiner platform.
Certification
After you have attended the training courses Basics 1 & 2, you can take an exam to acquire the “RapidMiner Analyst” certificate and show off your new qualification.
Price
Basics 2 € 1400 per participant (plus VAT)
Certificate Free of charge for participants; the exam can be taken on a voluntary basis at the end of the Basics 2 training*
*A later examination can be taken online at any time for an additional € 200.
Courses can only be offered where there are at least two participants. We will inform you whether the course can go ahead as soon as possible following your registration.
