Course details

ML/DL Best Practices: Machine Learning Workflow Best Practices

ML/DL Best Practices: Machine Learning Workflow Best Practices


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

This 12-video course explores essential phases of machine learning (ML), deep learning workflows, and data workflows that can be used to develop ML models. You will learn the best practices to build robust ML systems, and examine the challenges of debugging models. Begin the course by learning the importance of the data structure for ML accuracy and feature extraction that is wanted from the data. Next, you will learn to use checklists to develop and implement end-to-end ML and deep learning workflows and models. Learners will explore what factors to consider when debugging, and how to use flip points to debug a trained machine model. You will learn to identify and fix issues associated with training, generalizing, and optimizing ML models. This course demonstrates how to use the various phases of machine learning and data workflows that can be used to achieve key milestones of machine learning projects. Finally, you will learn high level-deep learning strategies, and the common design choices for implementing deep learning projects.



Expected Duration (hours)
0.9

Lesson Objectives

ML/DL Best Practices: Machine Learning Workflow Best Practices

  • discover the key concepts covered in this course
  • list the various phases of machine learning workflow that can be used to achieve key milestones of machine learning projects
  • recall the data workflows that are used to develop machine learning models
  • identify the differences between machine learning and deep learning and illustrate the phases of deep learning workflow
  • list the best practices that should be adopted to build robust machine learning systems, with focus on the evaluation approach
  • recall the challenges of debugging machine learning and deep learning projects and the factors that need to be considered while debugging
  • describe the approach of debugging trained machine learning models using flippoints
  • recognize the benefits of implementing machine learning checklists and the process of building checklists that can be used to work through applied machine learning problems
  • describe checklists for debugging neural networks, the steps involved in identifying and fixing issues associated with training, and generalizing and optimizing machine learning models
  • recall the checklists for implementing end-to-end machine learning and deep learning workflows that should be adopted to build optimized machine learning and deep learning models
  • describe the high-level deep learning strategies and the common design choices for implementing deep learning projects
  • summarize the key concepts covered in this course
  • Course Number:
    it_mlmdbpdj_01_enus

    Expertise Level
    Intermediate