About this Course

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Learner Career Outcomes

50%

started a new career after completing these courses

43%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level
Approx. 14 hours to complete
English

What you will learn

  • Assess the feasibility of your own ML use case and its ability to meaningfully impact your business.

  • Identify the requirements to build, train, and evaluate an ML model.

  • Define data characteristics and biases that affect the quality of ML models.

  • Recognize key considerations for managing ML projects.

Learner Career Outcomes

50%

started a new career after completing these courses

43%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level
Approx. 14 hours to complete
English

Instructor

Offered by

Placeholder

Google Cloud

Syllabus - What you will learn from this course

Content RatingThumbs Up94%(8,212 ratings)Info
Week
1

Week 1

27 minutes to complete

Module 1: Introduction

27 minutes to complete
1 video (Total 5 min), 3 readings
1 video
3 readings
How to download course resources2m
How to send feedback10m
Course Slides10m
1 hour to complete

Module 2: Identifying business value for using ML

1 hour to complete
4 videos (Total 25 min), 1 reading, 1 quiz
4 videos
AI vs ML vs Deep Learning10m
Phase 1: Assess feasibility4m
Practice assessing the feasibility of ML use cases7m
1 reading
Worksheet45m
1 practice exercise
Identifying business value for using ML10m
Week
2

Week 2

1 hour to complete

Module 3: Defining ML as a practice

1 hour to complete
9 videos (Total 42 min), 1 reading, 1 quiz
9 videos
Standard algorithm and data4m
Data quality8m
Predictive insights and decisions5m
More ML examples5m
Practice series: Analyze the ML use case1m
Saving the world's bees1m
Google Assistant for accessibility1m
Exercise review and Why ML now5m
1 reading
Module 3: Worksheet30m
1 practice exercise
Defining ML as a practice10m
3 hours to complete

Module 4: Building and evaluating ML models

3 hours to complete
6 videos (Total 56 min)
6 videos
Building labeled datasets18m
Training an ML model21m
General best practices3m
Introduction to hands-on labs6m
Lab 1: Review28s
1 practice exercise
Building and evaluating ML models20m
Week
3

Week 3

3 hours to complete

Module 5: Using ML responsibly and ethically

3 hours to complete
6 videos (Total 31 min)
6 videos
Google's AI Principles4m
Common types of human bias6m
Evaluating model fairness11m
Guidelines and Hands-on Lab5m
Lab 2: Review1m
1 practice exercise
Using ML responsibly and ethically20m
3 hours to complete

Module 6: Discovering ML use cases in day-to-day business

3 hours to complete
6 videos (Total 45 min), 1 reading, 2 quizzes
6 videos
Automate processes and understand unstructured data9m
Personalize applications with ML10m
Creative uses of ML13m
Sentiment analysis and Hands-on Lab2m
Lab 3: Review1m
1 reading
Sentiment Analysis Worksheet45m
1 practice exercise
Discovering ML use cases in day-to-day business30m
Week
4

Week 4

2 hours to complete

Module 7: Managing ML projects successfully

2 hours to complete
7 videos (Total 48 min)
7 videos
Data strategy (pillars 1–3)8m
Data strategy (pillars 4–7)6m
Data governance8m
Build successful ML teams7m
Create a culture of innovation and Hands-on Lab8m
Lab 4: Review1m
1 practice exercise
Managing ML projects successfully20m
8 minutes to complete

Module 8: Summary

8 minutes to complete
1 video (Total 8 min)
1 video

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