Course details

Refactoring ML/DL Algorithms: Refactor Machine Learning Algorithms

Refactoring ML/DL Algorithms: Refactor Machine Learning Algorithms


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

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.



Expected Duration (hours)
1.0

Lesson Objectives

Refactoring ML/DL Algorithms: Refactor Machine Learning Algorithms

  • 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
  • Course Number:
    it_mlrmdadj_02_enus

    Expertise Level
    Intermediate