Model Management: Building Machine Learning Models & Pipelines

Machine Learning    |    Intermediate
  • 11 videos | 31m
  • Includes Assessment
  • Earns a Badge
Rating 4.0 of 40 users Rating 4.0 of 40 users (40)
In this course, you will explore various approaches to building and implementing machine learning (ML) models and pipelines and will learn how to manage classification and regression problems. Begin this 11-video course by taking a look at the differences between ML models and ML algorithms. You will go on to learn about the different types of ML models and will then explore the approaches to developing and building them. Discover how to create and save ML models by using scikit-learn, and learn to recognize the various models that can be used to manage classification and regression problems. Explore how to build ML pipelines and then examine the prominent tools that can be used. You will learn how to implement scikit-learn ML pipelines, and in the final tutorial, learners will recall the steps involved in iterative machine learning model management and the associated benefits. In the concluding exercise, you will be asked to build ML models and pipelines by using scikit-learn.

WHAT YOU WILL LEARN

  • Recognize the differences between machine learning models and algorithms
    Identify the different types of machine learning models
    Describe the approaches and steps involved in developing machine learning models
    Create and save machine learning models using scikit-learn
    List machine learning models that can be used to manage classification and regression problems
  • Build machine learning pipelines
    List prominent tools that can be used to build machine learning pipelines
    Implement machine learning pipelines using scikit-learn
    Recall the steps involved in iterative machine learning model management and the associated benefits
    Build machine learning models and pipelines using scikit-learn

IN THIS COURSE

  • 1m 37s
  • 4m 17s
    After completing this video, you will be able to recognize the differences between machine learning models and algorithms. FREE ACCESS
  • Locked
    3.  Machine Learning Model Types
    2m 1s
    In this video, you will learn how to identify the different types of machine learning models. FREE ACCESS
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    4.  Machine Learning Model Development
    2m 39s
    Upon completion of this video, you will be able to describe the approaches and steps involved in developing machine learning models. FREE ACCESS
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    5.  Creating and Saving ML Models with scikit-learn
    3m 31s
    During this video, you will learn how to create and save machine learning models using the scikit-learn library. FREE ACCESS
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    6.  Models for Regression and Classification Management
    3m 40s
    Upon completion of this video, you will be able to list machine learning models that can be used to manage classification and regression problems. FREE ACCESS
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    7.  Building Machine Learning Pipelines
    2m 30s
    Find out how to build machine learning pipelines. FREE ACCESS
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    8.  Machine Learning Pipeline Tools
    2m 53s
    After completing this video, you will be able to list prominent tools that can be used to build machine learning pipelines. FREE ACCESS
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    9.  Machine Learning Pipeline Implementation
    3m 26s
    In this video, find out how to implement machine learning pipelines using scikit-learn. FREE ACCESS
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    10.  Iterative Machine Learning Model
    2m 24s
    After completing this video, you will be able to recall the steps involved in iterative machine learning model management and the benefits associated with it. FREE ACCESS
  • Locked
    11.  Exercise: Build Machine Learning Models & Pipelines
    2m 2s
    In this video, you will learn how to build machine learning models and pipelines using the scikit-learn library. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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