Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)

Description

At Sixe Engineering 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

Target audience

• Data scientists
• Business analysts
• Clients who want to learn about machine learning models

Desired Prerequisites:

• Knowledge of your business requirements

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 machine learning models

• Taxonomy of machine learning models

• Identify measurement levels

• Taxonomy of supervised models

• Build and apply models in IBM SPSS Modeler

Supervised models: Decision trees – CHAID

• CHAID basics for categorical targets

• Include categorical and continuous predictors

• CHAID basics for continuous targets

• Treatment of missing values

Supervised models: Decision trees – C&R Tree

• C&R Tree basics for categorical targets

• Include categorical and continuous predictors

• C&R Tree basics for continuous targets

• Treatment of missing values

Evaluation measures for supervised models

• Evaluation measures for categorical targets

• Evaluation measures for continuous targets

Supervised models: Statistical models for continuous targets – Linear regression

• Linear regression basics

• Include categorical predictors

• Treatment of missing values

Supervised models: Statistical models for categorical targets – Logistic regression

• Logistic regression basics

• Include categorical predictors

• Treatment of missing values

Supervised models: Black box models – Neural networks

• Neural network basics

• Include categorical and continuous predictors

• Treatment of missing values

Supervised models: Black box models – Ensemble models

• Ensemble models basics

• Improve accuracy and generalizability by boosting and bagging

• Ensemble the best models

Unsupervised models: K-Means and Kohonen

• K-Means basics

• Include categorical inputs in K-Means

• Treatment of missing values in K-Means

• Kohonen networks basics

• Treatment of missing values in Kohonen

Unsupervised models: TwoStep and Anomaly detection

• TwoStep basics

• TwoStep assumptions

• Find the best segmentation model automatically

• Anomaly detection basics

• Treatment of missing values

Association models: Apriori

• Apriori basics

• Evaluation measures

• Treatment of missing values

Association models: Sequence detection

• Sequence detection basics

• Treatment of missing values

Preparing data for modeling

• Examine the quality of the data

• Select important predictors

• Balance the 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

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• United Kingdom: London