Course 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.
Learning Objectives
Model Evaluation and Selection
- 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
Machine Learning With AWS
- 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
Practice: Bias and Variance
- define bias, variance, and tradeoffs