KNIME for Data Science and Data Cleaning

Focused View

Dan We

2:49:44

124 View

01.01-welcome to knime.mp4

00:47

01.02-copying or moving files with knime.mp4

07:35

01.03-reading multiple excel files-potential errors and solutions.mp4

08:22

01.04-reading multiple excel files-benefits of loops.mp4

10:26

01.05-excel files with different table structures in knime.mp4

08:19

01.06-useful nodes-column aggregations.mp4

13:07

01.07-countries-data cleaning challenge.mp4

08:48

01.08-merge table challenge in knime.mp4

07:06

01.09-a json file challenge in knime.mp4

13:36

01.10-create the neural network h5 model file to be used in knime.mp4

08:48

01.11-mismatching addresses-introduction to similarity search in knime.mp4

08:12

01.12-tensorflow neural network regression implementation in knime.mp4

10:15

01.13-transfer learning in knime using python scripts.mp4

11:42

01.14-introduction to nlp in knime part 1.mp4

08:57

01.15-nlp in knime part 2-data preprocessing and cleaning.mp4

09:09

01.16-nlp in knime part 3-bag of words and document vector.mp4

08:56

01.17-nlp in knime-choose ml algorithm and score our model.mp4

07:30

01.18-congratulations.mp4

00:23

02.01-copying or moving files with knime.mp4

05:03

02.02-reading multiple excel files-benefits of loops.mp4

07:15

02.03-excel files with different table structure in knime.mp4

05:28

9781801071413 Code.zip

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

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Data Science

Data Analysis

Dan We

Instructor's Courses

Dan We is a 32-year-old entrepreneur, data scientist, and data analytics/visual analytics consultant. He holds a master’s degree and is certified in Power BI as a qualified associate in Tableau. He is currently working in business intelligence and helps major companies get key insights from their data in order to deliver long-term growth and outpace their competitors. He is committed to supporting other people by offering them educational services to help them accomplish their goals and become the best in their profession or explore a new career path.

PacktPub

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Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.