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

4.65 (383 reviews)
Udemy
platform
English
language
Other
category
Barbora Stetinova, MBA
instructor
Data analyzing and Machine Learning Hands-on with KNIME
2,161
students
4.5 hours
content
Jan 2021
last update
$59.99
regular price

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)

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

Why take this course?

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.


Screenshots

Data analyzing and Machine Learning Hands-on with KNIME - Screenshot_01Data analyzing and Machine Learning Hands-on with KNIME - Screenshot_02Data analyzing and Machine Learning Hands-on with KNIME - Screenshot_03Data analyzing and Machine Learning Hands-on with KNIME - Screenshot_04

Reviews

Balakrishna
January 30, 2021
For a starter, the course is great. There are some volume and other quality issues but they are liveable. But, need more focus on the PMML Model. Are there any other export options available besides PMML?
Abdallah
January 6, 2021
The course was really great, the narrator or the teacher was very clear and their english was rather easy to understand. However, the pace was bit fast and some nodes were not explained in details. I felt there was a bit of rushing in explaining stuff. I did not attend a lot of courses but I would prefer things bit slower, especially for someone who is learning this for the first time. Also, i felt the examples were rather simple and did not cover many stuff in KNIME
Horacio
January 6, 2021
It s great course and the teacher it s fabulous, answer all question and very quickly. Excellent choice!
Ashlin
January 2, 2021
Audio sound is very bad.. i hardly hear anything in the course. Course is good and the instructor made things very simple
Julius
December 6, 2020
The content was good but they earlier videos had extremely poor video quality and some ties the instructor moves too fast
Ram
December 5, 2020
I have been using Knime. So a lot of this course content was a repetition for me. But I have learnt a few new tricks. Audio quality was not very good. Looking for a more advanced course.
Juan
October 28, 2020
Es mi primer curso de Knime, y fue muy interesante, aprendí desde lo mas básico que es lo que necesitaba.
Georgi
October 13, 2020
Do not recommend. Put aside the many typos and the, at times, bad English and confusing explanation, my overall impression is that the course was done in a hurry. The instructor forgets steps in the workflows and then goes back to execute/ fix them, which is fine but gives the impression it is done with little prior preparation. I feel the course is stretched way more than it should be, and some parts i.e. visualization are just repeated for the sake of making the course longer. The materials and data we will need are at the end of the course instead of before or right after the video in which they are introduced. As in other KNIME courses, I feel there is very little emphasis on machine learning for the sake of switching colours on diagrams and annotating steps in the workflow. And lastly, this is subjective, but I feel you are overselling your brand waaay to much. I do not want to see your company logo at 3 different places on the screen nearly half the time. I am in no way connected to or benefit from this, but in my opinion, Dan We's course achieves more in less time. Nevertheless, I will not be refunding the course because I appreciate the time and effort to make this course, especially on a somewhat niche tool like KNIME.
Prasad
July 23, 2020
the sound could have been better. the course gave a good over of capabilities of knime. It will be good if we have samples across different domains
Daniela
June 29, 2020
A very complete course, well explained and entertaining ! It has served me very well to reinforce content and to learn new techniques. Thank you very much. I hope take another KNIME course with you !
Kiran
May 17, 2020
Amazing Course and Amazing instructor with Proper sound knowledge about the course. Instructor was able to deliver what was expected from the module.
Satyaprakash
May 14, 2020
More details would have been better. Looks more technical and I am a technical person. More information on PMML reader and writer should be made available. In video, not explained properly. Not explained how to take the final output from Knime to other machines.
Vladimír
April 11, 2020
A very good intro to the Knime topic, and to the machine learning techniques. Knime is really a very nice tool, for data preparation, data analysis, machine learning and automation without coding. The course helped me to jump quickly to the topic. Thank you.
Bo
April 9, 2020
First part is really a good introduction to KNIME. Some differences with latest versions (i.e. 4.x) of KNIME for ML made it more interesting to actually know what is going on to get the right results.
Júlio
February 29, 2020
É um bom curso introdutório. Ele tem um começo bem detalhado e cobre bem os principais fundamentos de tratamento e limpeza de dados. Em seguida apresenta de forma clara o processo padrão de particionar->treinar->predizer->visualizar. Todavia, me deu a impressão que a parte final foi um pouco corrida para introduzir algumas técnicas de modelagem, mas sem o mesmo cuidado tido com a parte introdutória de tratamento de dados. Apesar disto, não compromete o curso como um todo. Ele atende bem ao seu propósito como curso introdutório. Recomendo para quem quer aprender a usar o KNIME.

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Related Topics

Aprendizaje automático
Data Analysis
Data Science
KNIME
2112202
udemy ID
12/28/2018
course created date
11/21/2019
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