Coursera – AI for Good Specialization
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Learn AI’s role in addressing complex challenges. Build skills combining human and machine intelligence for positive real-world impact using AI
What you’ll Learn:
• Master a step-by-step framework for the development of AI projects.
• Explore real-world case studies related to public health, climate change, and disaster
management.
• Analyze data and build AI models for projects focused on air quality, wind energy,
biodiversity monitoring, and disaster management.
Skills you’ll Gain:
• Natural Language Processing
• Topic Model
• Air Quality Monitoring
• AI for Good Project Framework
• Biodiversity Monitoring
• Time Series
• Damage Assessment
• Jupyter Notebooks
• Supervised Learning
• Exploratory Data Analysis
• Computer Vision
• Wind Power Generation Modeling
Specialization – 3 course series
The AI for Good Specialization showcases how AI can be part of the solution when it comes to addressing some of the world’s biggest challenges in areas like public health, climate change, and disaster management.
• AI and Public Health
• AI and Climate Change
• AI and Disaster Management
In these courses, you’ll learn from instructor Robert Monarch, who has over 20 years of experience building AI products in industry and working at the intersection of AI and public health and disaster management. Robert is also the author of Human-in-the-Loop Machine Learning, a book focused on human-centered AI applications.
Throughout the courses, you’ll hear from experts working on AI for Good initiatives aimed at addressing social and environmental issues. By combining human and machine intelligence, real-world datasets, best practices around data privacy, and ethical considerations, you’ll develop the knowledge and fundamental skills to tackle your own AI for good projects.
These courses were built in partnership with researchers at the Microsoft AI for Good Lab who offered their subject matter expertise throughout the development of the program. We are also grateful to Sasha Luccioni, Climate Lead and Researcher at HuggingFace for her help in forming the high-level program structure, outlining what kinds of topics and case studies would work best for these courses, and recruiting many of the experts that either appear in guest speaker videos or have contributed behind the scenes.
Applied Learning Project:
• Use neural networks and other AI techniques to estimate air quality throughout the city of
Bogotá, Colombia.
• Develop an AI model to make wind power generation more predictable by providing
forecasts 24 hours into the future.
• Apply computer vision techniques to detect and classify animals for the purpose of
biodiversity monitoring.
• Build an image classification pipeline to perform damage assessment using satellite images
taken after Hurricane Harvey in the U.S. in 2017.
• Use natural language processing techniques to analyze trends in a corpus of text messages
sent in the aftermath of the 2010 earthquake in Haiti.
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