Refactoring ML/DL Algorithms: Refactor Machine Learning Algorithms
Machine Learning
| Intermediate
- 11 videos | 58m 3s
- Includes Assessment
- Earns a Badge
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
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Discover the key concepts covered in this courseDescribe approaches for selecting an appropriate machine learning implementationSpecify the steps involved in designing machine learning algorithmsDescribe the impact of refactoring machine learning codeRecognize the principles of designing machine learning algorithmsCompare prominent machine learning algorithms and select the appropriate algorithm for diversified problem spaces
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Demonstrate how to refactor existing machine learning code that is written in pythonIdentify the essential approaches of managing technical debts in machine learning implementationsUse sonarqube to build code coverage for machine learning code that is written in pythonDescribe the approach of automatic clone recommendation for refactoring based on the present and the pastRecall the principles involved in designing machine learning algorithms and refactor machine learning code written in python
IN THIS COURSE
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59s
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5m 53sAfter completing this video, you will be able to describe approaches for selecting an appropriate machine learning algorithm. FREE ACCESS
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9m 47sAfter completing this video, you will be able to specify the steps involved in designing machine learning algorithms. FREE ACCESS
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6m 9sAfter completing this video, you will be able to describe the impact of refactoring machine learning code. FREE ACCESS
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4m 32sUpon completion of this video, you will be able to recognize the principles of designing machine learning algorithms. FREE ACCESS
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7m 24sDuring 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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3m 52sIn this video, you will learn how to refactor existing machine learning code written in Python. FREE ACCESS
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6m 8sIn this video, learn how to identify and manage the essential approaches of technical debt in machine learning implementations. FREE ACCESS
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5m 21sDuring 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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5m 18sUpon 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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2m 41sAfter 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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