Data analyzing and Machine Learning Hands-on with KNIME
[Only registered and activated users can see links. Click Here To Register...]
Data analyzing and Machine Learning Hands-on with KNIME
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 2.28 GB
Genre: eLearning Video | Duration: 48 lectures (4 hour, 3 mins) | Language: English
Hands-on crash course guiding through codeless, user-friendly, free data science software KNIME Analytics Platform
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 Description)
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 Descriptions 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.
The course has two main sections:
1. PRE-PROCESSING DATA: MODELING AND VISUALIZING DATA FRAMES IN GENERAL
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 Description, pie chart, scatter Description, box Description)
2. MACHINE LEARNING - REGRESSION AND CLASSIFICATION: 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 Descriptions 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
3. Decision Tree Regression
4. Random Forest Regression
5. Random Forest Classification
6. Polynomial Regression
7. Naive Bayes
8. K nearest neighbors
9. Gradient booster Regression
10. Gradient booster Classification
models 3 - 10 were added in the end of 2019.
I will also explain the Knime Analytics Platform environment, guide you through the installation , and show you where to find help and hints.
The course was done in KNIME analytics platform version 3.x (there can be minor differences in few nodes in comparison with 4.x version)
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
