- Four Basic Steps in Data Preparation - Oct 26, 2021.
What we would like to do here is introduce four very basic and very general steps in data preparation for machine learning algorithms. We will describe how and why to apply such transformations within a specific example.
- Data Science 101: Normalization, Standardization, and Regularization - Apr 20, 2021.
Normalization, standardization, and regularization all sound similar. However, each plays a unique role in your data preparation and model building process, so you must know when and how to use these important procedures.
- Batch Normalization in Deep Neural Networks - Aug 7, 2020.
Batch normalization is a technique for training very deep neural networks that normalizes the contributions to a layer for every mini batch.
- KDnuggets™ News 20:n17, Apr 29: The Super Duper NLP Repo; Free Machine Learning & Data Science Books & Courses for Quarantine - Apr 29, 2020.
Also: Should Data Scientists Model COVID19 and other Biological Events; Learning during a crisis (Data Science 90-day learning challenge); Data Transformation: Standardization vs Normalization; DBSCAN Clustering Algorithm in Machine Learning; Find Your Perfect Fit: A Quick Guide for Job Roles in the Data World
- Data Transformation: Standardization vs Normalization - Apr 23, 2020.
Increasing accuracy in your models is often obtained through the first steps of data transformations. This guide explains the difference between the key feature scaling methods of standardization and normalization, and demonstrates when and how to apply each approach.
- Normalization vs Standardization — Quantitative analysis - Apr 30, 2019.
Stop using StandardScaler from Sklearn as a default feature scaling method can get you a boost of 7% in accuracy, even when you hyperparameters are tuned!
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- Feature Engineering for Machine Learning: 10 Examples - Dec 21, 2018.
A brief introduction to feature engineering, covering coordinate transformation, continuous data, categorical features, missing values, normalization, and more.