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08-03-2024 11:13 AM - edited 08-03-2024 11:28 AM
For a UK Government Agency, I made a Comprehensive presentation titled " Feature Engineering for Data Engineers: Building Blocks for ML Success". I made an article of it in Linkedlin together with the relevant GitHub code. In summary the code delves into the critical steps of feature engineering, demonstrating how to handle missing values, encode categorical data, and prepare numerical features for modelling. By employing techniques like mean imputation and one-hot encoding, we establish a solid foundation for training complex models such as Variational Autoencoders (VAEs). This comprehensive approach empowers data scientists and data engineers to extract meaningful insights and build high-performing machine learning pipelines.
The full post is here
Feature Engineering for Data Engineers: Building Blocks for ML Success | LinkedIn
London
United Kingdom
view my Linkedin profile
https://en.everybodywiki.com/Mich_Talebzadeh
Disclaimer: The information provided is correct to the best of my knowledge but of course cannot be guaranteed . It is essential to note that, as with any advice, quote "one test result is worth one-thousand expert opinions (Werner Von Braun)".
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09-15-2024 11:48 PM
Hi,
Excellent presentation and article! Your insights on feature engineering and practical code examples are incredibly useful for building strong ML models. Thanks for sharing!
Thanks,
Anushree
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09-15-2024 11:48 PM
Hi,
Excellent presentation and article! Your insights on feature engineering and practical code examples are incredibly useful for building strong ML models. Thanks for sharing!
Thanks,
Anushree
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09-17-2024 04:16 AM
Many thanks for your kind words Anushree. Much appreciated.
London
United Kingdom
view my Linkedin profile
https://en.everybodywiki.com/Mich_Talebzadeh
Disclaimer: The information provided is correct to the best of my knowledge but of course cannot be guaranteed . It is essential to note that, as with any advice, quote "one test result is worth one-thousand expert opinions (Werner Von Braun)".
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2 weeks ago
This is a fantastic post! The detailed explanation of feature engineering, from handling missing values to using Variational Autoencoders (VAEs) for synthetic data generation, provides invaluable insights for improving machine learning models. The approach of combining various preprocessing techniques is a great solution for building robust, high-performance ML pipelines.
Could you kindly share on how we can create a feature store in in Databricks?
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