Applying Machine Learning

Machine Learning    |    Beginner
  • 13 videos | 32m 32s
  • Includes Assessment
  • Earns a Badge
Rating 4.3 of 164 users Rating 4.3 of 164 users (164)
Applying machine learning to problems can be a difficult task because of all the different models that are offered. Discover how to evaluate and select machine learning models and apply machine learning to a problem.

WHAT YOU WILL LEARN

  • 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
    Describe how to evaluate different models and select one

IN THIS COURSE

  • 2m 22s
    After completing this video, you will be able to describe the two main types of error in machine learning models and the tradeoff between them. FREE ACCESS
  • 1m 26s
    Upon completion of this video, you will be able to describe how to use cross-validation to show how well a model generalizes. FREE ACCESS
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    3.  Cross-validation in Python
    1m 49s
    After completing this video, you will be able to describe cross-validation in Python to obtain accurate evaluation scores. FREE ACCESS
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    4.  Metrics for Binary Classification Models
    2m 59s
    Upon completion of this video, you will be able to describe different metrics that can be used to evaluate binary classification models. FREE ACCESS
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    5.  Metrics for Non-binary Classification Models
    1m 38s
    Upon completion of this video, you will be able to describe different metrics that can be used to evaluate binary classification models. FREE ACCESS
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    6.  Classification Metrics in Python
    3m 1s
    Upon completion of this video, you will be able to describe common evaluation metrics for classification models. FREE ACCESS
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    7.  Metrics for Regression Models
    1m 29s
    After completing this video, you will be able to describe different metrics that can be used to evaluate regression models. FREE ACCESS
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    8.  Regression Metrics in Python
    1m 35s
    Upon completion of this video, you will be able to describe how to use Python to calculate common evaluation methods. FREE ACCESS
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    9.  Introduction to AWS Machine Learning
    2m 12s
    After completing this video, you will be able to describe AWS machine learning in standard English. FREE ACCESS
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    10.  Setting Up an AWS Environment for Machine Learning
    3m 18s
    During this video, you will learn how to set up an AWS environment and import data sources. FREE ACCESS
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    11.  Creating a Model in AWS
    3m 25s
    In this video, you will learn how to create a model with AWS. FREE ACCESS
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    12.  Training a Model and Making Predictions in AWS
    5m 7s
    In this video, learn how to set training criteria with AWS and train a model. FREE ACCESS
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    13.  Exercise: Evaluate and Select Models
    2m 11s
    Upon completion of this video, you will be able to describe how to evaluate different models and select one that is the best fit. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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