Identifying Opportunities for Machine Learning-Course Overview
Specialization Overview
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Instructor Introduction
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Course Overiew
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About the Course
Important Reminder
Identifying Opportunities for Machine Learning-Identifying Opportunities
Introduction & Objectives
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Identifying Opportunities
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Identifying Good Problems for ML
Validating Product Ideas
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Identifying Opportunities for Machine Learning-Do You Really Need ML?
Benefits of ML in Products
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ML vs. Heuristics
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Identifying Opportunities for Machine Learning-Review
Module Wrap-up
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Organizing ML Projects-Data Science Process
Introduction and Objectives
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ML Projects vs. Software Projects
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Why are ML Projects so Hard to Manage
CRISP-DM Data Science Process
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CRISP-DM Case Study
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Organizing ML Projects-Project Teams
Team Organization
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Organizing the Project
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Measuring Performance
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Organizing ML Projects-Review
Module Wrap-up
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Data Considerations-Sourcing Data
Introduction and Objectives
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Data Needs
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Data Collection
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Data Considerations-Data Pipelines
Data Governence & Access
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How We Improved Data Discovery for Data Scientists at Spotify
Data Cleaning
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Preparing Data for Modeling
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Reproducibility & Versioning
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Data Considerations-Review
Module Wrap-up
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ML System Design & Technology Selection-ML System Design
Introduction and Objectives
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ML System Design Considerations
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Cloud vs. Edge
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Online Learning & Inference
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ML on Big Data
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ML System Design & Technology Selection-ML Tools & Technologies
ML Technology Selection
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Common ML Tools
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Why Jupyter is Data Science's Computational Notebook of Choice
ML System Design & Technology Selection-Review
Module Wrap-up
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Model Lifecycle Management-Challenges of Deployed Models
Introduction and Objectives
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ML System Failures
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Google’s Medical AI was Super Accurate in a Lab. Real Life was a Different Story.
Model Lifecycle Management-Managing Deployed Models
ML System Monitoring
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Model Maintenance
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Model Versioning
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Organizational Considerations
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Model Lifecycle Management-Review
Module Wrap-up
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Model Lifecycle Management-Course Wrap Up
Course Wrap-up
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