SPSS Modeler: advanced machine learning models (0A039G)

• Teams combining Modeler with R or Python code who want to integrate it properly.
• Anyone who needs a generalized linear model and does not know which distribution to pick.
• Analysts sitting on free text they are not exploiting.

• Introduction to advanced models: taxonomy, supervised models and models that create natural groupings
• Grouping fields with Factor Analysis and Principal Components: assumptions, interpretation and scores
• Predicting with Nearest Neighbor Analysis: the basics, key issues and assessing model fit
• Advanced supervised models: Support Vector Machines, Random Trees and XGBoost
• Introduction to Generalized Linear Models: available distributions and link functions
• Combining supervised models: the Ensemble node, methods for categorical, flag and continuous targets, and meta-level modeling
• Using external machine learning models through SPSS Modeler extension nodes
• Analysing text data: text mining in data science, applications and modeling with text

• Labs on IBM SPSS Modeler with real data: streams built, run and fixed during the course.
• Taught directly by IBM Champion instructors from the SIXE team.
• Credly badges available: credly.com/organizations/sixe/badges
• Flexible format — online, classroom or onsite — in closed groups, priced per attendee, in the language of your choice (ES / EN / FR).

Can R or Python be used inside Modeler?
Yes, and that is one of the blocks: extension nodes let you call external models from inside the stream, and the course shows how it fits with the rest.

Does it cover text analysis?
It introduces it: what text mining is and how you model with text. The full course is Text Analytics (0A108G), two days.

Can you deliver it at our premises?
Yes, and remotely with a live instructor too. Closed groups of four to ten people, in Spanish, English or French.

• 0A079G — machine learning models
• 0A038G — advanced predictive modeling
• 0A108G — Text Analytics