Data analyzing and Machine Learning Hands-on with KNIME

Name: Data analyzing and Machine Learning Hands-on with KNIME
Rating: 4.5 (409 reviews)

Hands-on crash course guiding through codeless, user-friendly, free data science software KNIME Analytics Platform

Created byBarbora Stetinova, MBA

Last updated 8/2025

English

What you'll learn

Machine Learning in codeless KNIME Analytics Platform from A to Z – Classification and Regression
Machine Learning models - Regression (simple linear, multilinear, polynomial, decision tree, random forest)
Machine Learning models - Classification (decision tree, random forest, naive bayes, SVM, gradient booster)
Data preparation for the machine learning predictive model with KNIME nodes
Machine Learning model´s performance evaluation (confusion matrix, accuracy ratio, R squared)
Collecting different data sources at one place
Exploring data to understand its trend, relations etc.
Using and working with Metanodes and Components
Data normalization
Outliers detection
Understand KNIME environment, work with the workflow files and KNIME nodes
Transform data by using basic KNIME nodes
Visualize data by using charts, plots and statistics KNIME nodes (line plot, scatter plot, correlation matrix, box plot, histogram)
Understand the basic theory and its importance of the AI, Big Data, Data Science and Machine Learning including several techniques
Install and be able to work with the KNIME Analytics Platform environment
Find help and advice when working with KNIME

Course content

7 sections • 57 lectures • 4h 17m total length

Introduction to the Knime course1:47
Introduction to the course, content specification
Knime Analytics Platform environment description1:08
Description of the Knime platform environment, explanation how to use each sections in the Knime analytics platform.
If you are new to KNIME, please switch the interface into the classic one - see in the resources section.
Knime Analytics Platform installation3:45
We will guide you through the installation of the Knime analytics platform.
Please also, when installing new KNIME analytics platform, see the enclosed PDF document where you will find information about the difference between standard (older versions 3 - 4) and new 5 version.
There are launched 5.x versions which brought significant changes.
But do not worry, in the enclosed PDF you can find how to switch to classic environment. Also few nodes will have slightly different naming (like instead of Pivoting Pivot etc.).
Knime analytics platform installation0:37
Knime analytics platform versions0:11
If you are new to KNIME, please switch the interface into the classic one - see in the resources section.

Welcome to the first, data Manipulation and Visualisation part0:15
1. If you are new to KNIME, please switch the interface into the classic one - see in the resources section.
2. Due to often KNIME upgrades, the names of the nodes might sometimes be changed, e.g.:
- File Reader (for CSV reading) use CSV Reader in newer versions
- Excel Reader (XLS) use Excel Reader in newer versions
Acquiring data into KNIME workflow8:33
After this lecture you will be able to read data by using KNIME (xls and csv)
Basic work with KNIME nodes7:19
In this tutorial we will learn how to use the KNIME nodes
Merging the data8:34
We will merge data which we have read in the previous lecture
Table manipulation nodes - table information and transposing the table3:48
In this lecture we will learn how to get information about the data frames and we will learn how to transpose the table (switch columns and rows)
Row filters and row splitters9:11
After this lecture, you will be able to split your data into datasets and filter your data according to the selected values
Row transformation focused mainly on grouping and pivoting data5:59
After this lecture you will be able to partition your data, group them and pivot, similarly to the pivoting and grouping in MS Excel
Intro to column transformation options1:05
Due to often KNIME upgrades, the names of the nodes might sometimes be changed, e.g.:
- Column Renam use Column Renamer in newer versions
Columns binners5:44
We will create numeric binners of our numeric data into groups according the boundaries we will set up
Column converting part I.9:09
After this lecture you will be able to convert the data types, rename the columns, add constant value
Column converting part II.5:00
In this lecture we will count basic calculations by using the expressions in the math formula node
Column filtering3:22
We will filter our data set by using column filter node and missing value column node, so we will learn how to filter out certain columns.
Column split8:54
We will split our columns into more columns by using several splitting nodes
Missing values4:52
How to handle when having missing values? Use the missing values node and use more options in there
Date and time - part I.6:11
After this lecture you will be able to change the data types to date format and count the difference between two days
Date and time - part II.2:55
During this lecture you will see how easy is to extract different information from the date and time format, e.g. year, month, day etc.
Visualisation - Histogram6:09
Histogram / column charts enables to compare values of different classes in columns
Visualisation - Line plot5:39
To depict how the values of some variable develop over time is useful to illustrate by Line plot
Visualisation - Pie chart6:20
Check the size of certain class is high in comparison with a total size
Visualisation - Scatter plot8:26
By using scatter plot we check the relation between two variables
Visualisation - Box plot5:54
Learn about the value distribution.

Introduction to the topic1:16
Welcome in the Machine Learning section
Introduction to AI and Data Science2:39
To get the whole picture, we should understand AI, Data Science, Machine Learning and Big Data
Introduction to AI and Data Science1:15
To get the whole picture, we should understand AI, Data Science, Machine Learning and Big Data
Introduction to AI and Data Science4:48
To get the whole picture, we should understand AI, Data Science, Machine Learning and Big Data
Introduction to AI and Data Science2:07
To get the whole picture, we should understand AI, Data Science, Machine Learning and Big Data
Introduction to AI and Data Science1:56
To get the whole picture, we should understand AI, Data Science, Machine Learning and Big Data
Introduction to AI and Data Science4:35
To get the whole picture, we should understand AI, Data Science, Machine Learning and Big Data

Machine Learning introduction2:56
Welcome in the Machine Learning part
KNIME analytics platform folder preparation + Downloadable files1:56
Here we prepare the folder for our hands-on part. Please download all files.
Please be aware, that with the version KNIME 3.X there is a problem to load Heart Disease file including in some cases (7 rows) instead of numbers the sign ?.
For those of you reading the Heart disease file and getting error when using Excel reader node, use the file HeartDisease_noQM.xlsx and therefore you won´t need to use row filters as shown in the next video in the preprocessing part.
Introduction to Classification Machine Learning5:56
Learn about different classification ML techniques
Machine Learning - Classification - data collection2:21
Let´s collect data to our first workflow.

Please be aware, that with the version KNIME 3.X there is a problem to load Heart Disease file including in some cases (7 rows) instead of numbers the sign ?.
For those of you reading the Heart disease file and getting error when using Excel reader node, use the file HeartDisease_noQM.xlsx and therefore you won´t need to use row filters as shown in the video with the preprocessing part.
Machine Learning - Classification - data exploration5:31
To understand data we need to use different exploration techniques
Machine Learning - Classification - data preprocessing5:00
Before applying machine learning, we need to do data processing.

Please be aware, that with the version KNIME 3.X there is a problem to load Heart Disease file including in some cases (7 rows) instead of numbers the sign ?.
For those of you reading the Heart disease file and getting error when using Excel reader node, use the file HeartDisease_noQM.xlsx and therefore you won´t need to use row filters as shown in this video.
Machine Learning - Classification - data preprocessing6:32
Before applying machine learning, we need to do data processing
Machine Learning - Classification - data preprocessing7:07
Before applying machine learning, we need to do data processing
Machine Learning - Classification - decision tree6:23
We apply first classification algorithm - decision tree
Machine Learning - Classification - ensemble methods5:09
Let´s try another, more powerful methods
Machine Learning - Classification - SVM and N.Bayes4:32
Let´s try another methods
Introduction to Regression Machine Learning5:28
In the second part we will work on regression problem
Machine Learning - Regression - Data Collection3:54
Firstly, we load our data to KNIME
Machine Learning - Regression - data exploration5:06
To understand our data we need to use exploration nodes
Machine Learning - Regression - data preprocessing6:08
Before applying machine learning techniques we preprocess our data
Machine Learning - Regression - data preprocessing3:49
Before applying machine learning techniques we preprocess our data
Machine Learning - Regression - Machine Learning techniques9:26
Finally we can apply all machine learning techniques

Requirements

Access to computer or laptop with Windows (32bit or 64 bit), Linux (64bit) or Mac (64bit) and with permission to download software (if not, ask your administrator)
No prior knowledge required
Basic data analyzing experience in different programs, like MS Excel or SQL or Python etc. is added advantage

Description

The goal of this course is to gain knowledge how to use open source Knime Analytics Platform for data analysis and machine learning predictive models on real data sets.

The course has two main sections:

1. PRE-PROCESSING DATA: TRANSOFRMING AND VISUALIZING DATA FRAMES

In this part we will cover the operations how to model, transform and prepare data frames and visualize them, mainly:

table transformation (merging data, table information, transpose, group by, pivoting etc.)
row operations (eg. filter)
column operations (filtering, spiting, adding, date information, missing values, adding binners, change data types, do basic math operations etc.)
data visualization (column chart, line plot, pie chart, scatter plot, box plot)

2. MACHINE LEARNING - REGRESSION AND CLASSIFICATION: We will create machine learning models in standard machine learning process way, which consists in:

data collection with reading nodes into the KNIME software (the data frames are available in this course for download)
pre-processing and transforming data to get well prepared data frame for the prediction
visualizing data with KNIME visual nodes (we will create basic plots and charts to have clear picture about our data)
understanding what machine learning is and why it is important
creating machine learning predictive models and evaluating them:

Simple and Multiple linear Regression
Polynomial Regression
Decision Tree Classification
Decision Tree Regression
Random Forest Regression
Random Forest Classification
Naive Bayes
SVM
Gradient booster

I will also explain the Knime Analytics Platform environment, guide you through the installation , and show you where to find help and hints.

One lecture is focused on working with Metanodes and Components.

Who this course is for:

anyone searching user-friendly, easily understandable, codeless and highly useful tool for data analyzing and machine learning tasks without necessity to have programming skills
people working with several data sources of different file types
people working with data - both small and big data
anyone excited in learning new technologies in the data science field
people willing to learn and use new modern tools for data analyzing and machine learning

Data analyzing and Machine Learning Hands-on with KNIME

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 7min

OPTIONAL - KNIME software for codeless work with data (for KNIME begginers)21 lectures • 2hr 3min

OLD (Machine learning model A-Z: Acquiring data into the Knime workflows)2 lectures • 1min

Machine Learning model A-Z - introduction to the topic7 lectures • 19min

Machine Learning model A-Z - hands-on (Classification and Regression)17 lectures • 1hr 27min

Machine Learning - Churn classification model3 lectures • 17min

Extensions and conclusion2 lectures • 3min

Requirements

Description

Who this course is for: