AI and Climate Change
In this course, you’ll start with a review of the mechanisms behind anthropogenic climate change and its impact on global temperatures and weather patterns. You will work through two case studies, one using time series analysis for wind power forecasting and another using computer vision for biodiversity monitoring. Both case studies are examples of where AI techniques can be part of the solution when it comes to the mitigation of and adaptation to climate change.
Skills you'll learn
We may earn a commission if you enroll through our links — it never affects the price you pay.
Similar courses
Jetson Nano Starter to Pro - A Computer Vision Course
This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will explore the fascinating world of computer vision and artificial intelligence through the NVIDIA Jetson platform. By working through various modules, you'll gain a strong understanding of how to set up and optimize Jetson for AI tasks, with hands-on projects and practical applications of Jetson's capabilities. The course covers everything from bas
All levels
Amazon Kinesis Video Streams - Getting Started
With Amazon Kinesis Video Streams, you can build media streaming applications for Internet of Things (IoT) video devices and real-time computer vision machine learning (ML) applications. In this course, you will learn the benefits and technical concepts of Kinesis Video Streams. If you are new to the service, you will learn how to start using Kinesis Video Streams through a demonstration using the AWS Management Console. You will learn about the native architecture and how the built-in features can help you simplify image extraction through APIs or automated image extraction from metadata tag
All levels
Introduction to Computer Vision
In the first course of the Computer Vision for Engineering and Science specialization, you’ll be introduced to computer vision. You'll learn and use the most common algorithms for feature detection, extraction, and matching to align satellite images and stitch images together to create a single image of a larger scene. Features are used in applications like motion estimation, object tracking, and machine learning. You’ll use features to estimate geometric transformations between images and perform image registration. Registration is important whenever you need to compare images of the same s
All levels
Introduction to Computer Vision
Introduction to Computer Vision guides learners through the essential algorithms and methods to help computers 'see' and interpret visual data. You will first learn the core concepts and techniques that have been traditionally used to analyze images. Then, you will learn modern deep learning methods, such as neural networks and specific models designed for image recognition, and how it can be used to perform more complex tasks like object detection and image segmentation. Additionally, you will learn the creation and impact of AI-generated images and videos, exploring the ethical consideration
All levels