Altair Course: RapidMiner Machine Learning Introduction, Tile: RapidMiner Machine Learning Introduction Course Certificate

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

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

RapidMiner Machine Learning Introduction

Course Summary

RapidMiner Machine Learning Introduction Course Certificate

Welcome again

Please note if you wish to receive a certificate of course completion or resume the course where you stopped before, you must log into the Learning Management System before you start the course.