Data Transformation Techniques in Machine Learning, Normalization and Standardization (scaling)

This video explains, Data Transformation Techniques in Machine Learning. This video answers the top questions about:  - Different data transformation techniques. - What is normalization in data transformation techniques? - What is standardization / scaling in data transformation techniques? Data Transformation is use to convert data into a suitable format for modeling. Different data transformation techniques are: - Normalization & Standardization (scaling) - Encoding Categorical Variables (Label Encoding and One-Hot Encoding) - Log Transformation Normalization & Standardization (scaling) are essential preprocessing steps in machine learning, ensuring that features are on a comparable scale. This can lead to improved model performance and faster convergence during training. Chapters: 00:00 Introduction: Data Transformation Techniques 01:32 Introduction: Normalization and Standardization 01:55 Normalization. 02:39 Normalization Data Convergence Example 03:41 Normalization With Data Frame Example 04:19 Standardization / Scaling 04:55 Standardization / Scaling Data Convergence Example 05:46 Standardization / Scaling With Data Frame Example 07:27 When to Use Normalization vs. Scaling 07:01 Summary ================================== 🔥Python Tutorial for Programmers: https://www.youtube.com/playlist?list=PLM1rOqKFsPJJtiZhVZOOx9ZYVbeH7RuE- 🔥Python Data Type Tutorial for Programmers: https://www.youtube.com/playlist?list=PLM1rOqKFsPJIlJnVlfMQLFpGk1yj_300l 🔥Python Interview Questions: https://www.youtube.com/playlist?list=PLM1rOqKFsPJJr3wsCqkhrvUG_QszNqFbF ================================== Website:- https://programlover.com/machine-learning/preprocessing/data-integration-in-machine-learning Instagram:- https://www.instagram.com/programlover27/ Linkedin:- www.linkedin.com/in/program-lover ================================== #datascience , #machinelearning , #datatransformation , #datacleaning , #datapreprocessing , #programlover