Week 3: Advice for applying machine learning

This week you'll learn best practices for training and evaluating your learning algorithms to improve performance. This will cover a wide range of useful advice about the machine learning lifecycle, tuning your model, and also improving your training data.

Learning Objectives

• Evaluate and then modify your learning algorithm or data to improve your model's performance

• Evaluate your learning algorithm using cross validation and test datasets.

• Diagnose bias and variance in your learning algorithm

• Use regularization to adjust bias and variance in your learning algorithm

• Identify a baseline level of performance for your learning algorithm

• Understand how bias and variance apply to neural networks

• Learn about the iterative loop of Machine Learning Development that's used to update and improve a machine learning model

• Learn to use error analysis to identify the types of errors that a learning algorithm is making

• Learn how to add more training data to improve your model, including data augmentation and data synthesis

• Use transfer learning to improve your model's performance.

• Learn to include fairness and ethics in your machine learning model development Measure precision and recall to work with skewed (imbalanced) datasets

Advice for applying machine learning

Deciding what to try next - Video • Duration: 3 min

Evaluating a model - Video • Duration: 10 min

Model selection and training/cross validation/test sets - Video • Duration: 14 min

Practice quiz: Advice for applying machine learning

Practice quiz: Advice for applying machine learning

Bias and variance

Diagnosing bias and variance - Video • Duration: 11 min

Regularization and bias/variance - Video • Duration: 10 min

Establishing a baseline level of performance - Video • Duration: 9 min

Learning curves - Video • Duration: 12 min

Deciding what to try nextrevisited - Video • Duration: 8 min

Bias/variance and neural networks - Video • Duration: 10 min

Practice quiz: Bias and variance

Practice quiz: Bias and variance

Machine learning development process

Iterative loop of ML development - Video • Duration: 7 min

Error analysis - Video • Duration: 8 min

Adding data - Video • Duration: 14 min

Transfer learning: using data from a different task - Video • Duration: 12 min

Full cycle of a machine learning project - Video • Duration: 8 min

Fairness, bias, and ethics - Video • Duration: 9 min

Practice quiz: Machine learning development process

Practice quiz: Machine learning development process

Skewed datasets (optional)

Error metrics for skewed datasets - Video • Duration: 11 min

Trading off precision and recall - Video • Duration: 11 min

Practice Lab: Advice for Applying Machine Learning

Practice Lab: Advice for Applying Machine Learning