KNIME for Data Science and Data Cleaning

Name: KNIME for Data Science and Data Cleaning
Price: 134.99 USD
Author: Packt Publishing

Perform data science with KNIME. Learn how to do data cleaning, AI machine learning, ETL, and data preprocessing with KNIME

updated on icon Updated on Oct, 2023

language icon Language - English

person icon Packt Publishing

architecture icon IT & Software,KNIME

Lectures -21

Duration -2.5 hours

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Course Description

Data cleaning is always a big hassle, especially if we are short on time and want to deliver crucial data analysis insights to our audience. KNIME makes the data prep process efficient and easy. With KNIME, you can use the easy-to-use drag-and-drop interface, if you are not an experienced coder. But if you know how to work with languages such as R, Python, or Java, you can use them as well. This makes KNIME a truly flexible and versatile tool.

In this course, we will learn how to use additional helpful KNIME nodes not covered in the other two classes. Solve data cleaning challenges together for different datasets. Use pre-trained models in TensorFlow in KNIME (involves Python coding).

Also, learn the fundamentals for NLP tasks (Natural Language Processing) in KNIME using only KNIME nodes (without any additional coding).

By the end of this course, you will be able to use KNIME for data cleaning and data preparation without any code.

All the resources and support files for this course are available at https://github.com/PacktPublishing/KNIME-for-Data-Science-and-Data-Cleaning

Audience

This course is designed for aspiring data scientists and data analysts who want to work smarter, faster, and more efficiently. This course is also for anyone who wants to learn how to effectively clean data or encounter various data issues (for example, format) in the past and is looking for a solid solution, and who is familiar with KNIME as no basics are covered in this course. Note: Tableau Desktop and Microsoft Power BI Desktop are optional.

Goals

What will you learn in this course:

How to use TensorFlow in KNIME
How to do data science in KNIME with and without coding
How to solve data cleaning and data preparation challenges
How to replace Excel and start KNIME for ETL and data cleaning issues
Examples of data science machine learning workflows with KNIME

Prerequisites

What are the prerequisites for this course?

Basic knowledge of machine learning is certainly helpful for the later lectures in this course

KNIME for Data Science and Data Cleaning

Curriculum

Check out the detailed breakdown of what’s inside the course

Introduction
18 Lectures

Welcome to KNIME 00:47 00:47
Copying or Moving Files with KNIME 07:35 07:35
Reading multiple Excel files - Potential Errors and Solutions 08:22 08:22
Reading Multiple Excel Files - Benefits of Loops 10:26 10:26
Excel Files with Different Table Structures in KNIME 08:19 08:19
Useful Nodes - Column Aggregations 13:07 13:07
Countries - Data Cleaning Challenge 08:48 08:48
Merge Table Challenge in KNIME 07:06 07:06
A JSON File Challenge in KNIME 13:36 13:36
Create the Neural Network h5 Model File to be Used in KNIME 08:48 08:48
Mismatching Addresses - Introduction to Similarity Search in KNIME 08:12 08:12
TensorFlow Neural Network Regression Implementation in KNIME 10:15 10:15
Transfer Learning in KNIME Using Python Scripts 11:42 11:42
Introduction to NLP in KNIME Part 1 08:57 08:57
NLP in KNIME Part 2 - Data Preprocessing and Cleaning 09:09 09:09
NLP in KNIME Part 3 - Bag of Words and Document Vector 08:56 08:56
NLP in KNIME - Choose ML Algorithm and Score Our Model 07:30 07:30
Congratulations 00:23 00:23

Older Videos KNIME Version Before 4.3
3 Lectures

Instructor Details

Packt Publishing

Founded in 2004 in Birmingham, UK, Packt's mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals.

Working towards that vision, we have published over 6,500 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools.

As part of our mission, we have also awarded over $1,000,000 through our Open Source Project Royalty scheme, helping numerous projects become household names along the way.

Course Certificate

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