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Machine Learning for Data Mining

  • Course Code: Artificial Intelligence - Machine Learning for Data Mining
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 1 Days Audience: This course is geared for those who wants to Get efficient in performing data mining and machine learning using IBM SPSS Modeler

Course Snapshot 

  • Duration: 1 days 
  • Skill-level: Foundation-level Machine Learning for Data Mining skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Get efficient in performing data mining and machine learning using IBM SPSS Modeler 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

Working in a hands-on learning environment, led by our Machine Learning for Data Mining expert instructor, students will learn about and explore: 

  • Learn how to apply machine learning techniques in the field of data science 
  • Understand 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 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Hone your model-building skills and create the most accurate models 
  • Understand how predictive machine learning models work 
  • Prepare your data to acquire the best possible results 
  • Combine models to suit the requirements of different types of data 
  • Analyze single and multiple models and understand their combined results 
  • Derive worthwhile insights from your data using histograms and graphs 

Audience & Pre-Requisites 

This course is geared for attendees with Apache knowledge who wish to Get efficient in performing data mining and machine learning using IBM SPSS Modeler 

Pre-Requisites:  Students should have  

  • Basic to Python knowledge. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Introducing Machine Learning Predictive Models 
  • Introducing Machine Learning Predictive Models 
  • Characteristics of machine learning predictive models 
  • Types of machine learning predictive models 
  • Working with neural networks 
  • A sample neural network model 
  1. Getting Started with Machine Learning 
  • Getting Started with Machine Learning 
  • Demonstrating a neural network 
  • Support Vector Machines 
  • Demonstrating SVMs 
  1. Understanding Models 
  • Understanding Models 
  • Models 
  • Using graphs to interpret machine learning models 
  • Using statistics to interpret machine learning models 
  • Using decision trees to interpret machine learning models 
  1. Improving Individual Models 
  • Improving Individual Models 
  • Modifying model options 
  • Using a different model to improve results 
  • Removing noise to improve models 
  • Doing additional data preparation 
  • Balancing data 
  1. Advanced Ways of Improving Models 
  • Advanced Ways of Improving Models 
  • Combining models 
  • Using propensity scores 
  • Meta-level modeling 
  • Error modeling 
  • Boosting and bagging 
  • Predicting continuous outcomes 
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