IBM Training for Introduction to Machine Learning Models

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

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

Skill Level: Basic

Modality: SPVC – Self Paced Virtual Class

Duration: 2 Day/s

Starting Price: $ – 897

Overview:

This course teaches how to use machine learning models to predict categorical and continuous targets, to create natural groupings, and to find associations.

PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.
This course provides an introduction to supervised models, unsupervised models, and association models. This is an application-oriented course and examples include predicting whether customers cancel their subscription, predicting property values, segment customers based on usage, and market basket analysis.

If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course.

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Target Audience:

Data scientists

Business analysts

Clients who want to learn about machine learning models

Prerequisites:

Knowledge of your business requirements

Topic: Introduction to machine learning modelsTaxonomy 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 regressionLogistic regression basics Include categorical predictors Treatment of missing values Association models: Sequence detection Sequence detection basics 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 Preparing data for modeling Examine the quality of the data Select important predictors Balance the data.

IBM Training

Objective: Introduction to machine learning modelsTaxonomy 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 regressionLogistic regression basics Include categorical predictors Treatment of missing values Association models: Sequence detection Sequence detection basics 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 Preparing data for modeling Examine the quality of the data Select important predictors Balance the data

Category: Data, Analytics, and AI

Product Name:

IBM SPSS Modeler

Badge and Certification Info:

Badge Title:

Badge ID:

Brand: Analytics

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