Applying Machine Learning Overview/Description Target Audience Prerequisites Expected Duration Lesson Objectives Course Number Expertise Level Overview/Description
Applying machine learning to problems can be a difficult tasks because of all the different models that are offered. In this course you will learn how to evaluate and select machine learning models and apply machine learning to a problem.
Target Audience
Anyone interested in understanding machine learning and using it to solve problems
Prerequisites
None
Expected Duration (hours)
0.6
Lesson Objectives Applying Machine Learning
start the course
describe the two main types of error in machine learning models and the tradeoff between them
describe how to use cross-validation to show how generalized a model is
describe cross-validation in Python to obtain strong evaluation scores
describe different metrics that can be used to evaluate binary classification models
describe different metrics that can be used to evaluate non-binary classification models
describe common evaluation metrics for evaluating classification models
describe different metrics that can be used to evaluate regression models
describe how to use Python to calculate common evaluation methods
describe AWS machine learning
set up an AWS environment and import data sources
create a model with AWS
set training criteria with AWS and train a model
define bias, variance, and tradeoffs
Course Number: sd_exml_a06_it_enus
Expertise Level
Beginner