Data Mining with RapidMiner

Name: Data Mining with RapidMiner
Rating: 3.1 (39 reviews)

Created byEGMHAcademy EGMHAcademy

Last updated 3/2019

English

What you'll learn

Data Mining using RapidMIner

Course content

1 section • 39 lectures • 1h 22m total length

Getting Started2:36
Begin by visiting the official RapidMiner website to download the software, provide an email as required, download the Windows installer, and complete the installation to start data mining with RapidMiner.
Getting Started0:19
Open rapid mine studio, organize your notes, and set up to-do items to begin your data mining workflow.
Data Mining Process5:37
Explore the data mining process from business understanding to deployment, including data preparation, modeling with classification and regression, evaluation with accuracy and precision, and delivering results via reports or predictions.
Download Dataset1:11
Download datasets for statistical learning in data mining with RapidMiner, review the iris dataset, and search online to locate high risk datasets for analysis.
Read CSV0:49
Learn to load and import data from a csv file in RapidMiner, preparing the iris dataset for analysis.
Data Understanding - Statistics0:45
Explore data understanding and statistics in RapidMiner, examining metrics such as missing data, maximum values, the average, and the standard deviation to interpret data distribution.
Data Understanding - Scatterplot0:55
Explore data understanding in RapidMiner by visualizing raw data with a scatterplot, selecting better lines, and assessing NYSE data to highlight key patterns.
Data Understanding - Line0:44
Explore data understanding through data visualization and line analysis to interpret how line representations convey essential data patterns.
Data Understanding - Bar0:37
Develop data understanding with bar visualizations in RapidMiner, modify the y axis and assets, and use tooltips to reveal insights.
Data Understanding - Histogram0:46
Develop practical data visualization skills by building and refining a histogram, exploring broader histogram types, and learning how to create a histogram and profile a line in RapidMiner.
Data Understanding - Boxplot0:32
Develop understanding of data via boxplots to visualize distributions and variability, enabling quick interpretation of data features in the data mining with RapidMiner course.
Data Understanding - Pie0:40
Explore data visualization by creating a pie chart in RapidMiner, adjust chart parameters and legend, and plug the pie chart into RapidMiner Studio.
Data Understanding - Scatterplot Matrix0:42
Explore data understanding with a scatterplot matrix in RapidMiner, expanding matrices to include more variables and visualize relationships across a broader set of data features.
Data Preparation - Normalization1:41
Explore data preparation with RapidMiner by applying normalization to Iris data, examining all four features, and generating normalized results.
Data Preparation - Replace Missing Values1:14
Learn how to replace missing values in RapidMiner as part of data preparation, and explore configuring settings to handle incomplete data effectively.
Data Preparation - Remove Duplicates1:08
learn how to remove duplicates in RapidMiner to clean your data, locate duplicate records in the dataset, and apply the remove duplicates step to produce a normal data set.
Data Preparation - Detect Outlier0:46
Learn to identify and remove an outlier using distance-based settings in RapidMiner during data preparation, highlighting practical steps to run and validate the data cleanup.
Modeling: Simple Linear Regressions3:07
Apply simple linear regression to predict numerical outcomes using a 75/25 train-test split, train the model to estimate alpha and beta, and use it to predict price from engine size.
Modeling: Simple Linear Regressions (RapidMIner)4:21
Learn how to build a simple linear regression model in RapidMiner, split data 70/30 for training and testing, select attributes, and evaluate predictions.
Modeling: KMeans Clustering3:05
Learn how the k-means clustering algorithm groups data into k clusters by assigning each point to the nearest centroid, recomputing centroids as means, and repeating until clusters stabilize.
Modeling: KMeans Clustering using RapidmIner1:51
Explore k-means clustering in RapidMiner by selecting attributes, handling categorical variables, and creating subsets to run a five-cluster model on your dataset.
MOdeling: Agglomeration CLustering3:45
Explore agglomeration clustering by computing pairwise distances between data points, iteratively merging the closest groups, and forming clusters until a single cluster remains.
MOdeling: Agglomeration CLustering using RapidMIner1:07
Learn how to perform agglomerative clustering using RapidMiner, building hierarchical clusters and evaluating clustering outcomes.
MOdeling: Decision Tree ID3 Algorithm9:15
Explore the ID3 decision tree algorithm, calculating entropy and information gain to select attributes and split data for classification in data mining with rapidminer.
MOdeling: Decision Tree ID3 Algorithm using RapidMIner2:19
Explore building a decision tree with the ID3 algorithm in RapidMiner, selecting attributes, assigning labels, and visualizing the resulting diagram with a 70/30 data split.
MOdeling: Decison Tree ID3 Algorithm using RapidMiner0:33
Develop a decision tree model using the ID3 algorithm to predict outcomes on a data subset and validate the RapidMiner workflow.
Evaluation: Decison Tree ID3 Algorithm using Rapidminer0:42
Evaluate a decision tree using the ID3 algorithm in RapidMiner, focusing on validation, performance, and classification, and interpret the conclusion matrix.
Modeling: KNN Classification3:50
Use k-nearest neighbors classification by computing distances to data points, selecting the top k neighbors, and predicting the most frequent class. Evaluate accuracy on training and test data.
Modeling: KNN Classification using RapidmIner1:06
Explore modeling with KNN classification using RapidMiner, comparing it to decision trees and CNN classification while preparing datasets and running predictions.
Evaluation: KNN Classification using RapidMIner0:44
Explore the evaluation of knn classification using RapidMiner, focusing on model validation, performance assessment, and interpreting output results.
Modeling: Naive Bayes Classification5:36
Explore Naive Bayes classification in this lecture, applying Bayes theorem to predict classes from categorical features, using independence assumptions and frequency tables on a real dataset.
Modeling: Naive Bayes Classification using RapidmIner0:58
Course data mining with RapidMiner demonstrates Naive Bayes classification, designing the model and running RapidMiner to produce predictions.
Evaluation: Naive Bayes Classification using RapidmIner1:10
Explore validating and evaluating Naive Bayes classification in RapidMiner, using performance metrics and the confusion matrix to measure model quality.
MOdeling: Neural Network5:44
Explore neural networks for classification and clustering, highlighting neurons, activation functions, backpropagation, weights, bias, and training iterations across multi-layer perceptron and deep learning.
Modeling: Neural Network using Rapidminer0:59
Learn how to build and run a neural network classification in RapidMiner, from selecting the neural network operator and feeding data to setting parameters and obtaining predictions.
Evaluation: Neural Network using RapidmIner1:20
Explore evaluating a neural network model in RapidMiner by validating predictions, examining performance classifications, and generating a conversion matrix to assess results.
What ALgorithm to Use?1:35
Discover how to choose machine learning and statistical learning algorithms for data mining, using classification for labeled data, and clustering, regression, and dimensionality reduction for others.
Model Evaluation3:45
Explore model evaluation across regression, classification, and clustering, using metrics like sum of squared errors, total sum of squares, and r-squared, and leverage confusion matrices to assess accuracy and precision.
Evaluation: K Fold Cross Validation4:32
Apply k-fold cross validation in RapidMiner to evaluate classification models and assess performance. Build a neural network and visualize results with a conversion matrix, normalization, and simple regression techniques.

Requirements

Basic COmputer Knowledge

Description

Data Mining with RapidMiner: Master CRISP-DM & Machine Learning (No Coding Required)

Learn hands-on data mining using RapidMiner and the CRISP-DM framework. Build, train, and evaluate prediction models like Decision Trees, Naïve Bayes, k-NN, and Neural Networks fast.

What You'll Learn

Master the end-to-end CRISP-DM data mining process: Data Understanding, Preparation, Modeling, and Evaluation.
Clean and prepare real-world datasets by handling missing values, normalizing data, detecting outliers, and removing duplicates.
Build predictive machine learning models including Decision Trees, Naïve Bayes, k-Nearest Neighbors (k-NN), Neural Networks, and Linear Regression.
Apply unsupervised learning techniques like K-Means and Agglomerative Clustering.
Rigorously evaluate model performance using k-Fold Cross-Validation and scoring metrics in RapidMiner.
Understand algorithm selection to pick the exact model for your specific business data.

Why Learn Data Analysis & Data Science?

According to SAS, here are 5 key reasons why data analytics skills remain in massive demand:

Sharpen Problem-Solving Skills: Build structured analytical thinking to solve complex problems in business and everyday life.
High Market Demand: A growing global skills shortage means data analysts and data scientists command high value across industries.
Analytics Is Everywhere: Every organization needs insights from their data to optimize processes and make strategic decisions.
Growing Importance: The volume of data is increasing exponentially—creating more high-paying job opportunities for those who can interpret it.
Versatile & Cross-Disciplinary: Analytics bridges computer science, mathematics, and business strategy—plus applications in IoT and Smart Cities.

Why Learn RapidMiner?

RapidMiner is one of the premier visual, code-free data science platforms designed to bridge the gap between complex raw data and practical business execution:

No-Code / Low-Code ML Mastery: Visual workflows allow you to prototype, train, and test machine learning models in minutes without spending hours syntax-debugging R or Python scripts.
Explainable & Auditable Analytics: Avoid "black box" outcomes. RapidMiner provides full visibility into model evaluation, feature weights, and validation metrics so you can defend your findings to stakeholders.
End-to-End Data Pipeline Discipline: Learn structured data mining methodology—data cleaning, outlier detection, and normalization—essential skills for preparing raw enterprise data for any analytical pipeline.
Industry Standard: Visual machine learning skills give you a solid foundation in data science concepts that easily transfer across tools, enterprise platforms, and automated machine learning (AutoML) systems.

Course Overview

This bite-sized, practical course teaches you how to perform data mining visually using RapidMiner—one of the industry's leading code-free data science platforms. Guided by the standard CRISP-DM methodology, you will walk step-by-step through raw data preparation to advanced model validation without writing a single line of code.

Course Content

1. Getting Started & Foundations

Introduction & Setup
The CRISP-DM Data Mining Process
Downloading & Importing Datasets (CSV)

2. Data Understanding & Exploratory Visualizations

Summary Statistics
Scatterplots & Scatterplot Matrices
Line Charts, Bar Charts, & Histograms
Boxplots & Pie Charts

3. Data Preparation & Cleaning

Data Normalization
Handling & Replacing Missing Values
Duplicate Removal
Outlier Detection Techniques

4. Machine Learning Modeling & Execution

Regression: Simple Linear Regression (Theory & RapidMiner Implementation)
Clustering: K-Means & Agglomerative Clustering
Classification Models:
- Decision Trees (ID3 Algorithm)
- k-Nearest Neighbors (k-NN)
- Naïve Bayes Classification
- Artificial Neural Networks

5. Model Evaluation & Optimization

Choosing the Right Algorithm for Your Data
Performance Evaluation Metrics
Reliable Testing via k-Fold Cross-Validation

Who This Course Is For

Beginners wanting to step into Data Science without learning complex code first.
Business analysts, marketers, and researchers looking to extract insights using RapidMiner.
Students preparing for the SVBook Advanced Certificate in Data Science at EGMHAcademy.

Who this course is for:

Beginner Data Scientist or Analyst interested in RapidMiner