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What you'll learn?
- 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
Course Overview
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:
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data collection with reading nodes into the KNIME software (the data frames are available in this course for download)
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pre-processing and transforming data to get well prepared data frame for the prediction
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visualizing data with KNIME visual nodes (we will create basic plots and charts to have clear picture about our data)
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creating machine learning predictive models and evaluating them:
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Simple linear Regression
- Polynomial Regression
- Decision Tree
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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.
Pre-requisites
- 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
Target Audience
- anyone searching user-friendly, easily understandable, codeless and highly useful tool for data analyzing and machine learning tasks without necessity to have programming skills
Curriculum 27 Lectures 01:51:32
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Section 1 : Introduction
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Section 2 : KNIME environment
- Lecture 1 :
- KNIME installation
- Lecture 2 :
- KNIME description
- Lecture 3 :
- KNIME workflow
- Lecture 4 :
- KNIME installation - versions
-
Section 3 : Data collection
- 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
- 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
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Section 5 : Machine Learning
- 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
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Section 6 : Conclusion
- Lecture 1 :
- Conclusion
- Lecture 2 :
- All documents are available on OneDrive
Our learners work at
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Instructor
149173 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.
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