Description
At SIXE we have been providing official IBM training around the world for over 12 years. Get the best training from our specialists in Europe. We have important discounts and offers for two or more students.
Course details
| IBM course code: 0A039G | Category: IBM SPSS Modeler / IBM SPSS Modeler |
| Delivery: Online & on-site** | Course length in days: 1 |
Target audience
- Data scientists
- Business analysts
- Experienced users of IBM SPSS Modeler who want to learn about advanced techniques in the software
Desired Prerequisites:
- Knowledge of your business requirements
- Required: IBM SPSS Modeler Foundations (V18.2) course (0A069G/0E069G) or equivalent knowledge of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and know the basics of modeling.
- Recommended: Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) course (0A079G/0E079G), or equivalent knowledge or experience with the product about supervised machine learning models (CHAID, C&R Tree, Regression, Random Trees, Neural Net, XGBoost), unsupervised machine learning models (TwoStep Cluster), and association machine learning models such as APriori.
Instructors
The great majority of the IBM courses we offer are taught directly by our engineers. This is the only way we can guarantee the highest quality. We complement all the training with our own materials and laboratories, based on our experience during the deployments, migrations and courses that we have carried out during all these years.
Added value
Our courses are deeply role oriented. To give an example, the needs for technology mastery are different for developer teams and for the people in charge of deploying and managing the underlying infrastructure. The level of previous experience is also important and we take it very seriously. That is why beyond (boring) commands and tasks, we focus on solving the problems that arise in the day to day of each team. Providing them with the knowledge, competencies and skills required for each project. In addition, our documentation is based on the latest version of each product.
Agenda and course syllabus
Introduction to advanced machine learning models
• Taxonomy of models
• Overview of supervised models
• Overview of models to create natural groupings
Group fields: Factor Analysis and Principal Component Analysis
• Factor Analysis basics
• Principal Components basics
• Assumptions of Factor Analysis
• Key issues in Factor Analysis
• Improve the interpretability
• Factor and component scores
Predict targets with Nearest Neighbor Analysis
• Nearest Neighbor Analysis basics
• Key issues in Nearest Neighbor Analysis
• Assess model fit
Explore advanced supervised models
• Support Vector Machines basics
• Random Trees basics
• XGBoost basics
Introduction to Generalized Linear Models
• Generalized Linear Models
• Available distributions
• Available link functions
Combine supervised models
• Combine models with the Ensemble node
• Identify ensemble methods for categorical targets
• Identify ensemble methods for flag targets
• Identify ensemble methods for continuous targets
• Meta-level modeling
Use external machine learning models
• IBM SPSS Modeler Extension nodes
• Use external machine learning programs in IBM SPSS Modeler
Analyze text data
• Text Mining and Data Science
• Text Mining applications
• Modeling with text data
Do you need to adapt this syllabus to your needs? Are you interested in other courses? Ask us without obligation.
Locations for on-site delivery
- Austria: Vienna
- Belgium: Brussels, Ghent
- Denmark: Cophenhagen
- Estonia: Tallinn
- Finland: Helsinki
- France: Paris, Marseille, Lyon
- Germany: Berlin, Munich, Cologne, Hamburg
- Greece: Athens, Thessaloniki
- Italy: Rome
- Louxemburg: Louxembourg (city)
- Netherlands: Amsterdam
- Norway: Oslo
- Portugal: Lisbon, Braga, Porto, Coimbra
- Slovakia: Bratislava
- Slovenia: Bratislava
- Spain: Madrid, Sevilla, Valencia, Barcelona, Bilbao, Málaga
- Sweden: Stockholm
- Turkey: Ankara
- United Kingdom: London
