Overview

This is a quick tour video demonstrating some of the features of RapidMiner 5 within 3 minutes.

RapidMiner Video Tutorial 1:   User Interface and First Process

This video tutorial discusses the following topics:

• First Start and User Interface
• Operators
• First Process

RapidMiner Video Tutorial 2:   Data Import and Repositories

This video tutorial discusses the following topics:

• Importing Data
• Using Data in Processes
• Meta Data

RapidMiner Video Tutorial 3:   Modeling and Scoring

This video tutorial discusses the following topics:

• Basic Terms
• Training a Data Mining Model
• Applying Models for Scoring

RapidMiner Video Tutorial 4:   Visualization

This video tutorial discusses the following topics:

• Visualizing Data
• Visualizing Models and other Results
• Result Overview

RapidMiner Video Tutorial 5:   Evaluation and Performance Estimation

This video tutorial discusses the following topics:

• Nested Subprocesses
• Evaluation by Cross Validation
• Calculation and Visualization of Performances

RapidMiner Video Tutorial 6:   Data Transformations

This video tutorial discusses the following topics:

• Basic Preprocessing Operators
• Meta Data Transformation and Propagation
• Quick Fixes

Neural Market Trends Tutorial 1

Thomas Ott: "A quick look at the new Rapidminer 5.0. In this video we check out how the GUI changed and how to load in an Excel spreadsheet and run a simple neural net through it."

Neural Market Trends Tutorial 2

Thomas Ott: "The second Rapidminer 5.0 tutorial. In this video we begin to recreate the original written NMT YALE/Rapidminer tutorials into version 5.0. This videos shows how to import training and prediction data, add a classification learner, and apply the model."

Neural Market Trends Tutorial 3

Thomas Ott: "In this video I discuss how to use a cross and simple validation operator to split your training data into two sets: training and validation data sets. I also highlight the new intuitive “quick fix” error solution suggestions in Rapidminer 5.0. Enjoy!"

Neural Market Trends Tutorial 4

Thomas Ott: "In this video I highlight the data generation capabilities for Rapidminer 5.0 if you want to tinker around, and how to use a Genetic Optimization data pre-processor within a nested nested experiment. Yes, you read that correctly, a nested nested experiment."

Neural Market Trends Tutorial 5

Thomas Ott: "In this video we continue where we left off in Video Tutorial #4. We discuss some of the parameters that are available in the Genetic Algorithm data transformers to select the best attributes in the data set. We also replace the first operator with another Genetic Algorithm data transformer that allows us to manipulate population size, mutation rate, and change the selection schemes (tournament, roulette, etc)."

Neural Market Trends Tutorial 6

Thomas Ott: "Calling all marketers! In this video we discuss how we can use Rapidminer to create a decision tree to help us find "sweet spots" in a particular market segment. This video tutorial uses the Rapidminer direct mail marketing data generator and a split validation operator to build the decision tree."

Neural Market Trends Tutorial 7

Thomas Ott: "In this tutorial, we highlight Rapidminer's weighting operator using an evolutionary approach. We use financial data to preweight inputs before we feed them into a neural network model to try to better classify a gold trend."

Neural Market Trends Tutorial 8

Thomas Ott: "In this video we review data discovery techniques for Financial Time Series data by calculating a Simple Moving Average (SMA), creating a non-linear trend line using a Neural Net operator, and creating a time series prediction line using a Neural Net operator."

Neural Market Trends Tutorial 9

Thomas Ott: "In this video we start building a financial time series model, using S&P500 daily OHLCV data, and the windowing, sliding validation, and forecasting performance operator. This is Part 1 of the video."

Neural Market Trends Tutorial 10

Thomas Ott: "In this video we continue building a financial time series model, using S&P500 daily OHLCV data, and the windowing, sliding validation, and forecasting performance operator. We test the model with some out of sample S&P500 data."