Last updated 1/2023Created by Dan WeMP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 ChGenre: eLearning | Language: English + srt | Duration: 27 Lectures ( 4h 21m ) | Size: 1.74 GB
New job opportunities might open up for you You might be able to increase your productivity and save in your data preparation tasks Hopefully a higher efficiency in data preparation and data science related work What kind of loops are available and how to use them in K Examples of data science machine learning workflows with K Enhance your basic K skills already acquired ( for example in my K crash course on udemy) How to use Python in K (Java and R could also be used but will not be the focus here) How to do DataScience in K WITH AND WITHOUT CODING No extra costs - K can be ed for free You should have worked with K before The program itself and the basics are covered in "K - a crash course for bners" which is also available on udemy Basic knowledge of machine learning is certainly helpful. Coding is not required but we learn how you can use python and use your python code in K Data science and Data cleaning and Data preparation with KHello everyone hope you are doing fine.Let’s face it. Data preparation ,data cleaning, data preprocessing (whatever you want to call it) is most often the most tedious and consuming work in the data science / data analysis area.So many people ask: How can we speed up the process and be more efficient?Well one option could be to use tools which allow us to speed up the process (and somes reduce the amount of code we need to write).Meet KA great tool which comes to our rescue. K allows us to do data preparation / data cleaning in a very appealing drag and drop interface. (No coding experience is required yet it still allows us if we want to use languages like R, Python or Java. So, we can code if we want but don’t have to!). The flexibility of K makes that happen. WITH K we can also do Data Science, so machine learning and AI with or without coding.And the best: The Desktop version is free!So, is it worth it to dive deeper into K? ABSOLUTELY!This course is the second K class and expands the knowledge you have acquired in the first class "K - a crash course for bners" which is also available on udemy.We do not cover the basics (e.g. the interface, basic data import and filter nodes,...) here. If you need to refresh your knowlege or you have not had the chance to learn the basics I would recommend to check the prior class first (which covers all the basics in a great case study!)In this class we dive intoefficient ways to import multiple files into Kloopswebscrapingscripting (using Python code in K)hyperparameter optimizationfeature selectionbasic machine learning workflows and helpful nodes for this in KIf that does not sound like fun, then what? So, if that is interesting to you then let’s get started!Are you ready? (Aspiring) data scientists (Aspiring) data analysts data scientists / analysts who want to work smarter faster and more efficient HomePage:
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