Refactoring ML/DL Algorithms: Refactor Machine Learning Algorithms

Machine Learning    |    Intermediate
  • 11 videos | 58m 3s
  • Includes Assessment
  • Earns a Badge
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This course explores how to select the appropriate algorithm for machine learning (ML), the principles of designing machine learning algorithms, and how to refactor machine ML code. In 11 videos, you will learn the steps involved in designing ML algorithms. The complexity in the algorithm is huge, and learners will observe how to write iterative and incremental code, and how to apply refactoring to it. This course next examines the types of ML problems, and classifies it into four categories, and how to classify machine learning algorithms. You will learn how to refactor existing ML code written in Python, and to launch and use PyCharm IDE. This course also demonstrates how to use PyCharm IDE on a specific project learners will create. You will examine the problems associated with technical debt in ML implementation, and how to manage it. Then you will learn to use SonarQube to build code coverage for machine learning code that are written in Python. Finally, this course examines automatic clone recommendations for refactoring, based on the present and the past.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    describe approaches for selecting an appropriate machine learning implementation
    specify the steps involved in designing machine learning algorithms
    describe the impact of refactoring machine learning code
    recognize the principles of designing machine learning algorithms
    compare prominent machine learning algorithms and select the appropriate algorithm for diversified problem spaces
  • demonstrate how to refactor existing machine learning code that is written in Python
    identify the essential approaches of managing technical debts in machine learning implementations
    use SonarQube to build code coverage for machine learning code that is written in python
    describe the approach of automatic clone recommendation for refactoring based on the present and the past
    recall the principles involved in designing machine learning algorithms and refactor machine learning code written in Python

IN THIS COURSE

  • 59s
  • 5m 53s
    After completing this video, you will be able to describe approaches for selecting an appropriate machine learning algorithm. FREE ACCESS
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    3.  Machine Learning Algorithm Design
    9m 47s
    After completing this video, you will be able to specify the steps involved in designing machine learning algorithms. FREE ACCESS
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    4.  Impact of Refactoring on Machine Learning
    6m 9s
    After completing this video, you will be able to describe the impact of refactoring machine learning code. FREE ACCESS
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    5.  Algorithm Design
    4m 32s
    Upon completion of this video, you will be able to recognize the principles of designing machine learning algorithms. FREE ACCESS
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    6.  Machine Learning Algorithm Comparison
    7m 24s
    During this video, you will learn how to compare prominent machine learning algorithms and select the appropriate algorithm for different problem spaces. FREE ACCESS
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    7.  Refactor Machine Learning Code
    3m 52s
    In this video, you will learn how to refactor existing machine learning code written in Python. FREE ACCESS
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    8.  Managing Technical Debt in Machine Learning
    6m 8s
    In this video, learn how to identify and manage the essential approaches of technical debt in machine learning implementations. FREE ACCESS
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    9.  SonarQube and Code Coverage
    5m 21s
    During this video, you will learn how to use SonarQube to measure code coverage for machine learning code written in Python. FREE ACCESS
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    10.  Automatic Clone Refactoring
    5m 18s
    Upon completion of this video, you will be able to describe the approach of automatic clone recommendation for refactoring based on the present and the past. FREE ACCESS
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    11.  Exercise: Refactoring Machine Learning Code
    2m 41s
    After completing this video, you will be able to recall the principles involved in designing machine learning algorithms and refactor machine learning code written in Python. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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