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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