RapidMiner Basics Part 2
Two-day class from 9:00 a.m. to 5 p.m.
Lunch and afternoon snacks will be provided.
Be sure to check out our RapidMiner Basics Part 1 course being held in the same location on directly before this event.
RapidMiner Basics Part 2 is a two day course focusing on data mining and predictive analytics with RapidMiner Studio. Over the course of two days students will expand their knowledge gained in RapidMiner Basics Part 1 and build a more sophisticated analytical model while reinforcing their familiarity with the graphical interface and all of the products features and functionality.
The course is structured in a mentoring fashion where the entire group performs as members of a data science team. After successfully completing this course, participants will have a solid understanding of how RapidMiner Studio functions. Participants will be able to prepare data, create and validate predictive models, deploy models, and will be ready to extend their knowledge to advanced topics such as RapidMiner Server: Web Apps and Deployment, Big Data Analtyics with RapidMiner Radoop, and Text Mining with RapidMiner.
Practical exercises during the course prepare students to take the knowledge gained and apply to their own respective data mining problems, solving them quickly and easily. Since the class labs are hands-on and performed on the participants’ personal laptops, students will take actual classwork home with them, which will provide a jumpstart to the real world.
Analysts, Developers, and Administrators
Basic knowledge of computer programs and mathematics
RapidMiner Basics Part 1
After the training, students will have the ability to:
- Perform all common data preparations
- Build sophisticated analytical predictive models
- Evaluate model quality with respect to different criteria
- Deploy analytical predictive models
- Business Case Changes
- Intro Course Recap
- Loading New Data
- Multiple Sources
- Understanding New Attributes
- Schema Relationships
- Data Preparation
- Multi-level Aggregation
- Set Theory
- Calculated Values
- Regular Expressions
- Changing Value Types
- Balancing Data
- Outlier Detection
- Feature Selection
- Dimensionality Reduction
- Predictive Models (sample varies)
- Random Forest
- k-Means Clustering
- Neural Networks
- Logistic Regression
- Meta Learning
- Model Evaluation
- Advanced Performance Criteria
- ROC Plots
- Comparison between Models
- Lift Chart
- Significance Tests
- Validation of Preprocessing and Preprocessing Models
- Logging Results
- Sharing Data, Models, and Processes
- Exporting Processes as Web Service
- Basics of Report Creation
- Managing Processes and Services
What to Bring
You must bring a laptop to class (Windows, Mac or Linux OS). For Windows, Java Runtime Environment (JRE) version 7 is required. For Mac and Linux, Java Development Kit (JDK) version 7 is needed. Students will be provided with links to install RapidMiner Studio 6 prior to the class.
Please email us at firstname.lastname@example.org no later than 10 days prior to the event to notify us that you cannot attend. No refunds will be given after this date.
NOTE: Classes require a minimum of 3 students seven days before the commencement of the course. If there are insufficient registrants, the class will be cancelled and all students will be refunded the full registration fee. Students should organize their travel arrangements accordingly and with this proviso.
Zeit und Ort
RapidMiner offers a variety of ways to learn and develop your skills with the RapidMiner product suite. Our training courses are the most efficient and effective way for data analysts, data scientists, and administrators to get started with RapidMiner. They are also the perfect preparation for our certification exams which can qualify you as a Certified RapidMiner Analyst and Certified RapidMiner Expert.