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

  • Tuition USD $1,650
  • Reviews star_rate star_rate star_rate star_rate star_half 1110 Ratings
  • Course Code 0A079G
  • Duration 2 days
  • Available Formats Classroom, Virtual
0A079G - Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)

Course Eligible for IBM Digital Badge

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.

Skills Gained

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

 

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

Who Can Benefit

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

Prerequisites

  • Knowledge of your business requirements

Course Details

Course Outline

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

When does class start/end?

Classes begin promptly at 9:00 am, and typically end at 5:00 pm.

Does the course schedule include a Lunchbreak?

Lunch is normally an hour long and begins at noon. Coffee, tea, hot chocolate and juice are available all day in the kitchen. Fruit, muffins and bagels are served each morning. There are numerous restaurants near each of our centers, and some popular ones are indicated on the Area Map in the Student Welcome Handbooks - these can be picked up in the lobby or requested from one of our ExitCertified staff.

How can someone reach me during class?

If someone should need to contact you while you are in class, please have them call the center telephone number and leave a message with the receptionist.

What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

What does GTR stand for?

GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.

Does ExitCertified deliver group training?

Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.

Does ExitCertified deliver group training?

Yes, we provide training for groups, individuals, and private on sites. View our group training page for more information.

Pretty concise course mainly covering AWS Redshift while briefly introducing practical use examples with other services for companies.

Lab infraestructure is very suitable and works pretty good help to understand better the concepts exposed during the training.

The course is well organized, and I would recommend it. The cadence can be faster.

This is my first training with ExitCertified and I truly enjoyed this learning experience. I have taken other classes where the instructor solicited students for so much input it was unclear who was teaching the class. Other experiences were filled with so much personal chatter the instructor had to rush thru the course material. This instructor was very professional and actually delivered the materially.

Exit Certified made the learning process fun and understandable . Highly Recommend !!!

2 options available

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  • Nov 23, 2020 Nov 24, 2020 (2 days)
    Location
    iMVP
    Language
    English
    Time
    9:30AM 5:30PM EST
    Enroll
    Enroll
  • Feb 1, 2021 Feb 2, 2021 (2 days)
    Location
    iMVP
    Language
    English
    Time
    9:30AM 5:30PM EST
    Enroll
    Enroll
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