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Course Skill Level:


Course Duration:

1 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with Apache and basic Python & Linux experience seeking proficiency in data mining & ML using IBM SPSS modeler

Who should attend & recommended skills

  • This course is geared for those with Apache knowledge who want to become efficient in performing data mining and machine learning using IBM SPSS Modeler.
  • Skill-level: Foundation-level Machine Learning for Data Mining skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

Machine learning (ML) combined with data mining can give you amazing results in your data mining work by empowering you with several ways to look at data. This course will help you improve your data mining techniques by using smart modeling techniques. This course will teach you how to implement ML algorithms and techniques in your data mining work. It will enable you to pair the best algorithms with the right tools and processes. You will learn how to identify patterns and make predictions with minimal human intervention. You will build different types of ML models, such as the neural network, the Support Vector Machines (SVMs), and the Decision tree. You will see how all these models works and what kind of data in the dataset they are suited for. You will learn how to combine the results of different models to improve accuracy. Topics such as removing noise and handling errors will give you an added edge in model building and optimization. By the end of this course, you will be able to build predictive models and extract information of interest from the dataset.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning for Data Mining expert instructor, students will learn about and explore:
  • How to apply machine learning techniques in the field of data science
  • Understanding when to use different data mining techniques, how to set up different analyses, and how to interpret the results
  • A step-by-step approach to improving model development and performance
  • Honing your model-building skills and create the most accurate models
  • How predictive machine learning models work
  • Preparing your data to acquire the best possible results
  • Combining models to suit the requirements of different types of data
  • Analyzing single and multiple models and understand their combined results
  • Deriving worthwhile insights from your data using histograms and graphs

Course breakdown / modules

  • Characteristics of machine learning predictive models
  • Types of machine learning predictive models
  • Working with neural networks
  • A sample neural network model

  • Demonstrating a neural network
  • Support Vector Machines
  • Demonstrating SVMs

  • Models
  • Using graphs to interpret machine learning models
  • Using statistics to interpret machine learning models
  • Using decision trees to interpret machine learning models

  • Modifying model options
  • Using a different model to improve results
  • Removing noise to improve models
  • Doing additional data preparation
  • Balancing data

  • Combining models
  • Using propensity scores
  • Meta-level modeling
  • Error modeling
  • Boosting and bagging
  • Predicting continuous outcomes