#39 Machine Learning Specialization [Course 1, Week 3, Lesson 4]

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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 3 (Classification), Lesson 4 (The problem of overfitting), Video 3 (Cost function with regularization).

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Giving your videos pertinent titles like for example in this case: "Cost function regularization" would not only make your videos easier and more pleasant to navigate, they would make the YouTube algorithm direct a lot more traffic to you.

phillustrator
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Sir in every video your dress code is same . Is this is your uniform ?

ElectraGenius
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I have a question: why is the regularization term divided by m (that stands for number of rows), instead of n (that stands for number of columns) since we are summing w-squared values for all columns (variables), wouldn't it be more appropriate to divide by number of columns to get the average for all columns? if the data has say 20000 rows (m=20k), and not more than say 200 columns (n=200), the regularization term will end up being an insignificantly small value because of division by m instead of n. Can anyone please clarify. Thanks.

nareshk