#26 Machine Learning Specialization [Course 1, Week 2, Lesson 2]

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.

This video is from Course 1 (Supervised Machine Learning Regression and Classification), Week 2 (Regression with multiple input variables), Lesson 2 (Gradient descent in practice), Video 2 (Feature scaling part 2).

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[armanwirawan]

thank you andrew, i know you don't have to do this but i want to mention how grateful I'am i'm currently doing IBM data science in coursera, this really helps their machine learning section

[DronPatel-wt]

00:03 Implement feature scaling to scale features with different ranges to have comparable ranges
01:00 Mean normalization rescales the features so that they are centered around zero.
01:58 Normalization techniques include range normalization and mean normalization.
03:00 Standard deviation and z-score normalization
03:55 Feature scaling aims to normalize the range of features.
04:55 Features can have different ranges of values
05:47 Rescaling features can help improve machine learning algorithms.
06:47 Feature scaling can help improve the speed of gradient descent.

[Neofiio]

In this way，i think normalization will lose some precision to some extent, but can speed up the calculation and reduce the cost

[myquestforknowledge]

But when to do each of them? which case suits standard scaling and which case suits mean normalization?