ProductizeML
  • ProductizeML
  • Introduction
    • Objectives
    • About the Course
    • Guidelines
    • Syllabus
    • After Completion
  • Machine Learning
    • Why ML, and why now
    • Supervised Learning
    • Unsupervised Learning
    • Deep Learning
    • ML Terminology
  • Data Management
    • Data Access
    • Data Collection
    • Data Curation
  • Train and Evaluate
    • Framework and Hardware
    • Training Neural Networks
    • Model Evaluation
  • Productize It
    • ML Life Cycle
    • Business Objectives
    • Data Preparation
    • Model Development
    • Train, Evaluate, and Deploy
    • A/B Testing
    • KPI Evaluation
    • PM Terminology
  • Resources
    • Readings
    • Courses
    • Videos
  • Hands-On
    • Python for Machine Learning
      • Python Installation
        • MacOS
        • Linux
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  1. Introduction

After Completion

In general terms, after completing this course participants will be more familiar and confident with:

  • Common and most-used AI, Machine Learning, Deep Learning, and Data Science terminology.

  • What someone can expect realistically from these systems to do and what they actually cannot do.

  • Abilities to understand what the opportunities of an AI/ML product are, and how these can impact their institution, organization, or personal project.

  • Identify and prioritize the highest value applications for machine learning and do what it takes to make them successful.

  • Computational resources and recommended frameworks.

  • Best practices on data collection and curation.

  • Training, optimization, and monitoring of Machine Learning models.

  • Instructions on how to build reproducible machine learning pipelines.

  • Create continuous and automated integration to deploy models.

  • When building a product, what things we want the AI solution to improve or enhance when compared to traditional solutions.

  • Recommendations on how to deliver AI/ML-generated predictions to users.

  • Understand and practice with real-world cases and users' data.

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Last updated 2 years ago

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