In this course, you will be introduced to the basics of artificial intelligence and machine learning and how they are applied in real-world scenarios in the AI for Good space. You will also be introduced to a framework for problem solving where AI is part of the solution. The course concludes with a case study featuring three Jupyter notebook labs where you’ll create an air quality monitoring application for the city of Bogotá, Colombia.
AI and Public Health
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AI and Public Health
This course is part of AI for Good Specialization
Instructor: Robert Monarch
19,181 already enrolled
Gain insight into a topic and learn the fundamentals.
251 reviews
Beginner level
Recommended experience
Flexible schedule
9 hours to complete
Learn at your own pace
98%
Most learners liked this course
Skills you'll gain
- Supervised Learning
- Data Preprocessing
- Information Privacy
- Data Ethics
- Artificial Intelligence
- Machine Learning
- Applied Machine Learning
- Responsible AI
- Public Health
- Model Evaluation
- Environmental Monitoring
- Exploratory Data Analysis
- AI literacy
- Healthcare Ethics
- Machine Learning Algorithms
- Data Analysis
- Artificial Intelligence and Machine Learning (AI/ML)
Tools you'll learn
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
4 assignments
Taught in English
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This course is part of the AI for Good Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 3 modules in this course
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Showing 3 of 251
AS
Reviewed on Jul 14, 2023
Very comprehensive and accessible to a broad range of people.
DE
Reviewed on Sep 12, 2023
A good beginner course. Not super hands on, but gives a good overview of the process to building a good project.
TN
Reviewed on Jan 6, 2024
In the area of the Labs, I expected it to be slightly more mentally challenging. Giving participants opportunity to make informed guesses and then autocorrecting errors.
