ML/DL Best Practices: Machine Learning Workflow Best Practices

Machine Learning
  • 12 Videos | 57m 8s
  • Includes Assessment
  • Earns a Badge
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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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 11s
    UP NEXT
  • Playable
    2. 
    Machine Learning Workflow for Milestone
    7m 13s
  • Locked
    3. 
    Data Workflows for Machine Learning
    5m 42s
  • Locked
    4. 
    Phases of Deep Learning Flow
    7m 30s
  • Locked
    5. 
    Machine Learning Evaluation Approach
    5m 33s
  • Locked
    6. 
    Challenges of Debugging Machine Learning
    4m
  • Locked
    7. 
    Debugging Trained ML Models Using Flippoints
    3m 50s
  • Locked
    8. 
    Benefits of Machine Learning Checklists
    5m 45s
  • Locked
    9. 
    Checklists for Debugging Neural Network
    2m 48s
  • Locked
    10. 
    Machine Learning End-to-End Workflow Checklists
    3m 32s
  • Locked
    11. 
    Learning Strategy and Design Choices
    3m 22s
  • Locked
    12. 
    Course Summary
    1m 42s

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