Feature Scaling DemystifiedEssential Linear Scaling Techniques in Machine Learning! 0 like February 9, 2025Share this postLast Updated on February 10, 2025 by Editorial TeamAuthor(s): Harshit Dawar Originally published on Towards AI. Lets understand the most useful linear feature scaling techniques of Machine Learning (ML) in detail!This member-only story is on us. Upgrade to access all of Medium.Source: Image by NIR HIMI on UnsplashMachine Learning (ML) is a very vast field & requires a proper approach to formulate the solution for every problem, irrespective of the solution or problem being small scale or large scale.Since all ML models expect numeric input, it doesnt signify that passing the numeric features as they are fulfills the use case. Though the model will be trained, but the quality of the model is still the question.Many people who admire being an ML engineer or even existing ML engineers just send the data as it is (without the required processing) to the model for its training, without knowing that its not the optimized way. Its more like a scenario where you are communicating with a person in Hindi who only understands English, though the person can somehow try to understand what you are saying with the help of a translator, but at the end, its not effective. To make it effective, you must communicate with that person in English only.So, instead of following the group of people who are just trying to climb a big ladder with one step, a proper Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post