Data analyzing and Machine Learning Hands-on with KNIME
Data analyzing and Machine Learning Hands-on with KNIME
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.
Hands-on crash course guiding through codeless, user-friendly, free data science software KNIME Analytics Platform
Url: View Details
What you will 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)
Rating: 4.25
Level: All Levels
Duration: 4.5 hours
Instructor: Barbora Stetinova, MBA
Courses By: 0-9 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
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