WEEK 6

Posted 2021-01-24 keyshaw

WEEK 6

Advice for Applying Machine Learning

Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the learned models.
7 videos, 7 readings

Video: Deciding What to Try Next

Video: Evaluating a Hypothesis

Reading: Evaluating a Hypothesis

Video: Model Selection and Train/Validation/Test Sets

Reading: Model Selection and Train/Validation/Test Sets

Video: Diagnosing Bias vs. Variance

Reading: Diagnosing Bias vs. Variance

Video: Regularization and Bias/Variance

Reading: Regularization and Bias/Variance

Video: Learning Curves

Reading: Learning Curves

Video: Deciding What to Do Next Revisited

Reading: Deciding What to do Next Revisited

Reading: Lecture Slides

Programming: Regularized Linear Regression and Bias/Variance

Graded: Advice for Applying Machine Learning

Machine Learning System Design

To optimize a machine learning algorithm, you’ll need to first understand where the biggest improvements can be made. In this module, we discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data.
5 videos, 3 readings

Video: Prioritizing What to Work On

Reading: Prioritizing What to Work On

Video: Error Analysis

Reading: Error Analysis

Video: Error Metrics for Skewed Classes

Video: Trading Off Precision and Recall

Video: Data For Machine Learning

Reading: Lecture Slides

Graded: Machine Learning System Design