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Training Center MUKКурсыIBMIBM AI and AnalyticsIntroduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)

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

Код курса
0A079UA
Продолжительность
2 Дней, 16 Ак. Часов
Описание курса
Цели
Требования
Программа курса
Описание курса

Course overview

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.

Цели

Course objectives

  • Introduction to machine learning models
  • Supervised models: Decision trees — CHAID
  • Supervised models: Decision trees — C&R Tree
  • Evaluation measures for supervised models
  • Supervised models: Statistical models for continuous targets — Linear regression
  • Supervised models: Statistical models for categorical targets — Logistic regression
  • Association models: Sequence detection
  • Supervised models: Black box models — Neural networks
  • Supervised models: Black box models — Ensemble models
  • Unsupervised models: K-Means and Kohonen
  • Unsupervised models: TwoStep and Anomaly detection
  • Association models: Apriori
  • Preparing data for modeling
Требования

Audience

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

Prerequisites

  • Knowledge of your business requirements
Программа курса

Course topics

1. 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

2. Supervised models: Decision trees — CHAID

  • CHAID basics for categorical targets
  • Include categorical and continuous predictors
  • CHAID basics for continuous targets
  • Treatment of missing values

3. 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

4. Evaluation measures for supervised models

  • Evaluation measures for categorical targets
  • Evaluation measures for continuous targets

5. Supervised models: Statistical models for continuous targets — Linear regression

  • Linear regression basics
  • Include categorical predictors
  • Treatment of missing values

6. Supervised models: Statistical models for categorical targets — Logistic regression

  • Logistic regression basics
  • Include categorical predictors
  • Treatment of missing values

7. Supervised models: Black box models — Neural networks

  • Neural network basics
  • Include categorical and continuous predictors
  • Treatment of missing values

8. Supervised models: Black box models — Ensemble models

  • Ensemble models basics
  • Improve accuracy and generalizability by boosting and bagging
  • Ensemble the best models

9. 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

10. Unsupervised models: TwoStep and Anomaly detection

  • TwoStep basics
  • TwoStep assumptions
  • Find the best segmentation model automatically
  • Anomaly detection basics
  • Treatment of missing values

11. Association models: Apriori

  • Apriori basics
  • Evaluation measures
  • Treatment of missing values

12. Association models: Sequence detection

  • Sequence detection basics
  • Treatment of missing values

13. Preparing data for modeling

  • Examine the quality of the data
  • Select important predictors
  • Balance the data
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Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)
Код курса:
0A079UA
Продолжительность:
2 Дней, 16 Ак. Часов
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