Module 1: Introduction-Module 1: Welcome to the course
Introduction
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How to download course resources
How to send feedback
Module 2: Identifying business value for using ML-Module 2: Introduction
Introduction
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Module 2: Identifying business value for using ML-Understanding ML with examples
AI vs ML vs Deep Learning
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Module 2: Identifying business value for using ML-Module 2: Machine learning projects
Phase 1: Assess feasibility
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Practice assessing the feasibility of ML use cases
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Module 2: Identifying business value for using ML-Assignments
Worksheet
Module 3: Defining ML as a practice-Module 3: Introduction
Common ML problem types
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Module 3: Defining ML as a practice-Machine Learning Defined
Standard algorithm and data
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Data quality
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Predictive insights and decisions
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Module 3: Defining ML as a practice-Real-life Use Cases
More ML examples
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Practice series: Analyze the ML use case
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Module 3: Worksheet
Saving the world's bees
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Google Assistant for accessibility
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Exercise review and Why ML now
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Module 4: Building and evaluating ML models-Module 4: Introduction
Features and labels
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Module 4: Building and evaluating ML models-Tools to label datasets
Building labeled datasets
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Module 4: Building and evaluating ML models-Guidelines for training ML models
Training an ML model
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General best practices
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Module 4: Building and evaluating ML models-Getting started with Qwiklabs
Introduction to hands-on labs
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Lab 1: Review
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Module 5: Using ML responsibly and ethically-Module 5: Introduction
Human bias in ML
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Module 5: Using ML responsibly and ethically-Guiding Principles
Google's AI Principles
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Common types of human bias
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Module 5: Using ML responsibly and ethically-ML fairness
Evaluating model fairness
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Module 5: Using ML responsibly and ethically-Hands-on Lab 2
Guidelines and Hands-on Lab
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Lab 2: Review
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Module 6: Discovering ML use cases in day-to-day business-Module 6: Introduction
Replacing rule-based systems with ML
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Automate processes and understand unstructured data
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Module 6: Discovering ML use cases in day-to-day business-Beyond the basics
Personalize applications with ML
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Creative uses of ML
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Module 6: Discovering ML use cases in day-to-day business-Worksheet and Hands-on Lab 3
Sentiment analysis and Hands-on Lab
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Sentiment Analysis Worksheet
Lab 3: Review
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Module 7: Managing ML projects successfully-Module 7: Introduction
Key consideration 1: business value
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Module 7: Managing ML projects successfully-Key consideration 2
Data strategy (pillars 1–3)
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Data strategy (pillars 4–7)
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Module 7: Managing ML projects successfully-Key consideration 3 and 4
Data governance
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Build successful ML teams
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Module 7: Managing ML projects successfully-Key consideration 5 and Hands-on Lab
Create a culture of innovation and Hands-on Lab
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Lab 4: Review
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Module 8: Summary-Module 8: Course Summary
Summary
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