Google Machine Learning Courses: Machine Learning Crash Course, Part 2 - Loss

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Unleash your inner data scientist! In this video, we dive into a crucial aspect of machine learning: Loss. In part two of our linear regression series, we unravel the meaning and impact of Loss in machine learning models.

What is Loss, exactly? It's the measure of how “off” your model’s prediction is from the actual value. A lower Loss value means a more accurate prediction! We show how outliers can skew your model and increase Loss and cover L1 and L2, two key calculations for quantifying Loss.

We also explore the concept of Mean Absolute Error (MAE) and show you when to use MAE for your ML models.

Ready to enhance your machine learning skills? Let’s GO!
#machinelearning #loss #linearregression #MAE #MSE

Chapters:
00:00:00 Introduction
00:00:12 What you will learn
00:00:25 Linear Regression
00:00:33 What are Outliers?
00:01:23 What is Loss?
00:04:12 What is Distance of Loss?
00:05:42 Types of Loss
00:08:51 What is the L1 Norm?
00:10:51 L1 Norm: City Example
00:12:21 L1 Loss: Walking to Stores Example
00:14:29 What is Mean Absolute Error (MAE)?
00:16:30 What Does MAE Tell You?
00:18:05 When to use MAE?
00:20:27 Really Easy AI Channel Shoutouts
00:21:36 What is the L2 Norm?
00:23:27 L2 Norm Details
00:24:27 L2 Example
00:26:06 Understanding Mean Squared Error (MSE)
00:27:48 What does MSE tell you?
00:29:23 When to use MSE?
00:31:36 Linear Regression: Parameters exercise
00:33:42 Calculating Loss Example
00:36:49 Additional Considerations of MAE and MSE
00:38:03 Model trained with MSE Example
00:39:06 Check Your Understanding
00:40:52 Key Terms

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