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IT & Software Other IT & Software KNIME

Data analyzing and Machine Learning Hands-on with KNIME

Hands-on crash course guiding through codeless, user-friendly, free data science software KNIME Analytics Platform
Rating: 4.1 out of 54.1 (250 ratings)
1,389 students
Created by Barbora Stetinova, MBA
Last updated 1/2021
English
English [Auto]
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

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:

  1. data collection with reading nodes into the KNIME software (the data frames are available in this course for download)

  2. pre-processing and transforming data to get well prepared data frame for the prediction

  3. visualizing data with KNIME visual nodes (we will create basic plots and charts to have clear picture about our data)

  4. understanding what machine learning is and why it is important

  5. 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

Course content

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

  • Preview01:47
  • Preview01:08
  • Preview03:45
  • Preview00:37
  • Preview00:11

  • Welcome to the first, data Manipulation and Visualisation part
    00:15
  • Preview08:33
  • Basic work with KNIME nodes
    07:19
  • Merging the data
    08:34
  • Table manipulation nodes - table information and transposing the table
    03:48
  • Row filters and row splitters
    09:11
  • Row transformation focused mainly on grouping and pivoting data
    05:59
  • Intro to column transformation options
    01:05
  • Columns binners
    05:44
  • Column converting part I.
    09:09
  • Column converting part II.
    05:00
  • Column filtering
    03:22
  • Preview08:54
  • Missing values
    04:52
  • Date and time - part I.
    06:11
  • Date and time - part II.
    02:55
  • Visualisation - Histogram
    06:09
  • Visualisation - Line plot
    05:39
  • Visualisation - Pie chart
    06:20
  • Visualisation - Scatter plot
    08:26
  • Visualisation - Box plot
    05:54

  • OLD
    00:25
  • OLD - files for upload
    00:03

  • Introduction to the topic
    01:16
  • Introduction to AI and Data Science
    02:39
  • Introduction to AI and Data Science
    01:15
  • Introduction to AI and Data Science
    04:48
  • Introduction to AI and Data Science
    02:07
  • Introduction to AI and Data Science
    01:56
  • Introduction to AI and Data Science
    04:35

  • Preview02:56
  • KNIME analytics platform folder preparation + Downloadable files
    01:56
  • Preview05:56
  • Machine Learning - Classification - data collection
    02:21
  • Machine Learning - Classification - data exploration
    05:31
  • Machine Learning - Classification - data preprocessing
    05:00
  • Machine Learning - Classification - data preprocessing
    06:32
  • Machine Learning - Classification - data preprocessing
    07:07
  • Machine Learning - Classification - decision tree
    06:23
  • Preview05:09
  • Machine Learning - Classification - SVM and N.Bayes
    04:32
  • Introduction to Regression Machine Learning
    05:28
  • Machine Learning - Regression - Data Collection
    03:54
  • Machine Learning - Regression - data exploration
    05:06
  • Machine Learning - Regression - data preprocessing
    06:08
  • Preview03:49
  • Machine Learning - Regression - Machine Learning techniques
    09:26

  • Churn model - machine learning classification
    08:47
  • Churn model - new record prediction
    02:33
  • Churn model - Metanodes and Components
    05:45

  • Knime extensions
    00:13
  • Bonus and conclusion
    03:03

Instructor

Barbora Stetinova, MBA
ElderberryData
Barbora Stetinova, MBA
  • 4.1 Instructor Rating
  • 282 Reviews
  • 1,495 Students
  • 3 Courses

During 15 years working in Automotive industry I have earned experience in data science and machine learning, leading small team, working on strategical projects and in controlling.

Since Sept 2018 I have been working on implementing Data science and business intelligence projects in IT department.

In parallel, since Aug 2017, I am engaged as Analytical external consultant in different industries (retail, sensors, steel industry etc.) for Leadership Synergy Community.

In 2019 I have started to publish own e-learning courses and cooperate on e-learning projects with PACKT Publishing, both focused on data science with Python and Knime analytics platform.

I am motivated by learning new things, achieving goals and helping others.

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