Python vs. C#: Which Language Is Best for AI?
March 15, 2024
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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
Instructor: Robert Monarch
10,964 already enrolled
(275 reviews)
Recommended experience
Beginner level
No prior experience in AI or coding is necessary. This program is for learners with all types of backgrounds
(275 reviews)
Recommended experience
Beginner level
No prior experience in AI or coding is necessary. This program is for learners with all types of backgrounds
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.
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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.
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.
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.
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.
In this course, you will be introduced to the four phases of the disaster management cycle; mitigation, preparation, response, and recovery. You’ll work through two case studies in this course. In the first, you will use computer vision to analyze satellite imagery from Hurricane Harvey in 2017 to identify damage in affected areas. In the second, you will use natural language processing techniques to explore trends in aid requests in the aftermath of the 2010 earthquake in Haiti.
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Our world is facing many urgent challenges, and we will need skilled individuals across all types of roles and skillsets to address them. To be able to most effectively address these challenges with the help of advanced technologies like AI, learning the framework, principles, and skills included in this specialization will be critical.
This is a beginner-friendly specialization with minimal background knowledge required. Some prior experience in working with data, like doing some basic analysis and visualization of data using tools like a spreadsheet, is recommended. The Python programming language is used for the ungraded labs in these courses, but previous experience with Python programming is not required. This is a great place to start if you’re new to AI and want to learn how it can be applied to real-world challenges.
Yes. No previous programming experience is required to take this Specialization.
If you’re looking to apply those skills in an AI for Good project, then yes! Although the labs for this course will be beginner-level, you’ll be ready to get more creative and manipulate the data used in this course in more advanced ways.
If you’re looking for only AI skill-building, we recommend starting with AI for Everyone or Machine Learning Specialization. This course does build skills in AI, but focuses on their use in AI for Good contexts.
We recommend taking the courses in the prescribed order for a logical and thorough learning experience.
Please use the Learner Help Center for questions about your subscription.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.