Data analyzing and Machine Learning Hands-on with KNIME
- 0.0
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Brief Introduction
Hands-on crash course guiding through codeless, user-friendly, free data science software KNIME Analytics PlatformDescription
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.
Requirements
- 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
Knowledge
- 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
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