RapidMiner Machine Learning Introduction

Course Summary

Altair has vision to make data analytics simple enough for all users, but scalable, governed, and safe enough for all enterprises. RapidMiner is the enterprise-ready data science platform that amplifies the collective impact of your people, expertise and data for breakthrough competitive advantage.
Here, we cover both theory and hands-on practice with the basic techniques for building correctly validated Machine Learning Models. We cover some of the most common model types and how to build RapidMiner processes to train and evaluate those models. The course can help you prepare for the Machine Learning Professional Exam. We do not cover the answers to all questions on the exam, instead we ask you to take ownership for learning, understanding, and practicing the topics that we outline.

Prerequisites:
Required – Install RapidMiner Studio and make sure you can open it. Recommended – Look at the Getting Started series on academy.rapidminer.com.

The training consists of four sessions. There is one session per day which lasts for 2 hours.


Session 1 Session 2 Session 3 Session 4
Introduction
  • RapidMiner Introduction
  • Add a note
  • Save the process with the note
  • Drag in an operator
  • Look at operator help
  • Cover Repositories at this time
  • The RapidMiner “GoTo” Places
Introduction to Machine Learning
  • Introduction
  • k-NN
 Introduction to Machine Learning
  • Model Validation
  • Normalize and Group Models
Supervised Learning
  • Regression: Linear, Logistic, GLM
  • Naïve Bayes
 Supervised Learning
  • Decision Trees
Unsupervised Learning
  • Attribute Correlation
  • Clustering
  • Association Mining
 Feature Engineering
  • Introduction to Feature Engineering
  • Feature Weight
Auto Model
  • Demo Clustering
  • Demo Supervised Learning
  • Demo Deployment tools
Conclusions


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