Codeless Machine Learning In Open-source KNIME Analytics Platform

Hans-on machine learning in KNIME

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Last Updated: 2023-03-21 11:03:25 English Course Type: Self Learning

Machine Learning in KNIME Analytics Platform – Classification and Regression
Data preparation for the machine learning predictive model with KNIME nodes
Machine Learning performance evaluation
Collecting different data sources at one place
Transforming data by using basic KNIME nodes
Visualizing data
Install and be able to work with the KNIME Analytics Platform environment

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.

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)
creating machine learning predictive models and evaluating them:

Simple linear Regression
Polynomial Regression
Decision Tree
Random Forest Regression
Naive Bayes
KNN
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.

Access to computer or laptop with Windows (32bit or 64 bit), Linux (64bit) or Mac (64bit) and with permission to download softwares (if not, ask your administrator to download it for you – it is common at company´s computers)
No prior knowledge required

anyone searching user-friendly, easily understandable, codeless and highly useful tool for data analyzing and machine learning tasks without necessity to have programming skills

Section 1 : Introduction 1 Lectures 00:01:43
Lecture 1 :
Introduction Preview
Section 2 : KNIME environment 4 Lectures 00:08:06
Lecture 1 :
KNIME installation
Lecture 2 :
KNIME description
Lecture 3 :
KNIME workflow
Lecture 4 :
KNIME installation - versions
Section 3 : Data collection 5 Lectures 00:05:31
Lecture 1 :
Data collection
Lecture 2 :
Files to be downloaded
Lecture 3 :
Files to be downloaded II
Lecture 4 :
Files to be downloaded III
Lecture 5 :
Files to be downloaded IV
Section 4 : Data preparation and transformation 6 Lectures 00:39:06
Lecture 1 :
Data preparation - for classification
Lecture 2 :
Data preparation - for classification II
Lecture 3 :
Data preparation - for regression
Lecture 4 :
Data visualization I
Lecture 5 :
Data visualization II
Lecture 6 :
Data visualization III
Section 5 : Machine Learning 9 Lectures 00:54:03
Lecture 1 :
Machine Learning intro
Lecture 2 :
Machine learning - decision tree
Lecture 3 :
Machine learning - regression
Lecture 4 :
Machine learning - regression II
Lecture 5 :
Machine learning - decision tree for regression
Lecture 6 :
Machine Learning - random forest
Lecture 7 :
Machine learning - multiple and polynomial regression
Lecture 8 :
Machine learning - KNN and Naive Bayes
Lecture 9 :
Machine learning - gradient booster
Section 6 : Conclusion 2 Lectures 00:03:03
Lecture 1 :
Conclusion
Lecture 2 :
All documents are available on OneDrive

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Barbora Stetinova,

149225 Course Views

3 Courses

Experienced in Data Science and Business Intelligence (Machine Learning&Deep Learning projects, BI) for different fields, like Production, Logistics, Finance, Sales in automotive industry. Engaged as a business analyst consultant for different industries such as retail, sensorics, building, automotive etc., at Leadership Synergy Community. Publishing Data science e-learning courses under ElderberryData brand (e.g. Introduction to Big Data and Artificial Intelligence for managers, Machine Learning with KNIME analytics platform, Data manipulation and visualisation with Knime, Python and Excel, MS Excel crash course). Participating on educational e-learning projects and technical reviews of Python Machine learning and Deep learning courses with PACKT Publishing. Member of the DataCup 2019 winner team. I am motivated by learning new things and helping others.