Data analyzing and machine learning Hands-on with KNIME
3.7 (21 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
159 students enrolled

Data analyzing and machine learning Hands-on with KNIME

Hands-on crash course guiding through highly intuitive and modern open source data science Knime Analytics Platform
3.7 (21 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
159 students enrolled
Last updated 3/2019
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This course includes
  • 3.5 hours on-demand video
  • 18 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • create machine learning models in Knime Analytics Platform from A to Z – classification and regression
  • create machine learning models - Regression (simple linear, multilinear, polynomial, decision tree, random forest, gradient booster)

  • create machine learning models - Classification (decision tree, random forest, naive bayes, KNN, gradient booster)

  • prepare the data for the machine learning predictive model by using basic manipulating KNIME nodes
  • Evaluate the performance of the machine learning predictions (confusion matrix, accuracy ratio, scatter plot)
  • work with several different file’s data sources at one place
  • work with the workflow files and Knime nodes
  • acquire data into the Knime workflow
  • manipulate the data by using basic Knime nodes
  • visualize the data by using plots and statistics Knime nodes
  • understand the basic theory of the machine learning
  • install and understand the Knime Analytics Platform environment
  • find help and advice when working with Knime
Requirements
  • 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
  • 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.

We will create machine learning models within the standard machine learning process way, which consists from:

- acquiring data by 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)

- creating machine learning predictive models and evaluating them:

1. Decision Tree Classification

2. Simple linear Regression


from 11.2.2019 newly added:

3. Decision Tree Regression

4. Random Forest Regression

5. Random Forest Classification

6. Polynomial Regression (+ info about multi linear Regression - for Knime same nodes)

7. Naive Bayes

8. K nearest neighbors

9. Grandient booster Regression

10. Grandient booster Classification


I will also explain the Knime Analytics Platform environment, guide you through the installation  and show where to find help.


Who this course is for:
  • anyone searching user-friendly, easily understandable 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 things in the data science field
  • people willing to learn and use new modern tools for data analyzing and machine learning
Course content
Expand all 36 lectures 03:24:28
+ Introduction
3 lectures 06:57

Introduction to the course, content specification

Preview 01:43

We will guide you through the installation of the Knime analytics platform

Preview 03:17

Description of the Knime platform environment, explanation how to use each sections in the Knime analytics platform

Preview 01:57
+ Machine learning theoretical basis
1 lecture 06:53
Machine learning theoretical basis
06:53
+ Acquiring and pre-processing data by using KNIME nodes to get well prepared data
15 lectures 01:32:55

After this lecture you will be able to read data by using KNIME (xls and csv)

Acquiring data into KNIME workflow
08:13

In this tutorial we will learn how to use the KNIME nodes

Basic work with KNIME nodes
07:37

We will merge data which we have read in the previous lecture

Merging the data
10:18

In this lecture we will learn how to get information about the data frames and we will learn how to transpose the table (switch columns and rows)

Table manipulation nodes - table information and transposing the table
03:50

After this lecture, you will be able to split your data into datasets and filter your data according to the selected values

Row filters and row splitters
08:57

After this lecture you will be able to partition your data, group them and pivot, similarly to the pivoting and grouping in MS Excel

Row transformation focused mainly on grouping and pivoting data
07:13
Intro to column transformation options
00:47

We will create numeric binners of our numeric data into groups according the boundaries we will set up

Columns binners
05:38

After this lecture you will be able to convert the data types, rename the columns, add constant value

Column converting part I.
09:11

In this lecture we will count basic calculations by using the expressions in the math formula node

Column converting part II.
05:05

We will filter our data set by using column filter node and missing value column node, so we will learn how to filter out certain columns.

Column filtering
03:16

We can split our columns by using several splitting nodes into more columns

Column split
08:42

How to handle when having missing values? Use the missing values node and use more options in there

Missing values
04:37

After this lecture you will be able to change the data types to date format and count the difference between two days

Date and time - part I.
05:44

During this lecture you will see how easy is to extract different information from the date and time format, e.g. year, month, day etc.

Date and time - part II.
03:47
+ Machine learning model A-Z: Acquiring data into the Knime workflows
2 lectures 08:23

We will create new workflows we will us for our machine learning predictive models.

After watching this video, please download three uploaded excel files.

Preview 02:52

In the acquiring lecture we will upload our three files into already created workflows.

Acquiring data into the Knime workflows
05:31
+ Machine learning model A-Z: Data manipulation and preparation for the model
3 lectures 23:28

In this lecture there will be explained how to use manipulation nodes, for instance - we will join two tables, we use filtering, resorting, grouping, pivoting.

We will work on the first workflow with Car datasets.

Data preparation and manipulation with the Dataset Cars I.
08:03

In this lecture there will be prepared the data for the machine learning prediction.

We will use math formula node, date extractor and more.

We will work on the first workflow with Car datasets.

Data preparation and manipulation with the Dataset Cars II.
08:23

In this lecture there will be prepared the data for the machine learning prediction.

We will use resorters, format changing nodes, filtering. 

We will work on the second workflow with Sales headcounts datasets.

Data preparation and manipulation with the Dataset Sales
07:02
+ Machine learning model A-Z: Data visualisation and statistics
3 lectures 15:39

Purposes of the data visualisation description

Introduction to the data visualisation
01:57

Data visualisation by using box plot, scatter plot and basic statistics overview.

We will work on the first Car dataset

Data visualisation with the Car dataset
08:04

Data visualisation by using scatter plot, line plot and pie charts.

We will work on the second Sales dataset

Data visualisation with the Sales dataset
05:38
+ Machine learning model A-Z: predictive models (regression and classification)
3 lectures 15:02

We will create the decision tree classification model based on already prepared data we have done in last chapters. Also, we create the model to be able predict data when new data available.

Preview 07:36

We will create the linear regression model based on already prepared data we have done in last chapters. Your task will be to finalize the model to be able predict data when new data available.

In the next lecture you can check your result.

Machine learning predictive model - linear regression + individual work
02:58

Please check and compare our results

Machine learning predictive model - linear regression - individual work result
04:28
+ Bonus and conclusion
1 lecture 03:03

You will be able to find help and template workflows.

There are uploaded pictures of finished workflows we have done together.

All downloadable documents are available on : https://1drv.ms/f/s!AolTGH3TVJWGhRtpLJYUFSVBeL-e

Bonus and conclusion
03:03
+ Newly added machine learning techniques
5 lectures 32:08

After this lecture you will be able to create decision tree for numerical prediction

Machine learning model - Decision tree for regression prediction
04:34

We will create random forest techniques for our both projects

Machine learning model - Random forest for both supervised methods
05:31

In some cases depending on the character of the data inside our data set is more efficient to use instead of standard linear regression the polynomial one.

Machine learning model - Polynomial regression
04:13

For classification methods called Naive Bayes built on the Bayes probability theorm and KNN searching for nearest neighbors and assigning the class accordingly

Machine learning model - Naive Bayes and KNN
04:45

In this lecture we will create second machine learning technique from the ensemble learning group, which is the gradient boost model (and random forest we have already done).

Also, we will create for each regression model prediction the scatter plot to evaluate the single models in the chart form to easily read where the prediction was right and where wrong.

Machine learning model - Grandient Boost regression and classification
13:05