AI for Software Developers

The evolving world of artificial intelligence (AI) brings both opportunities and risks. To protect assets, organizations must understand how to secure their AI systems. This in-depth course delves into the AI security landscape, addressing vulnerabilities like prompt injection, denial of service attacks, model theft, and more. Learn how attackers exploit these weaknesses and gain hands-on experience with proven defense strategies and security APIs. Discover how to securely integrate LLMs into your applications, safeguard training data, build robust AI infrastructure, and ensure effective human-AI interaction. By the end of this course, you'll be equipped to protect your organization's AI assets and maintain the integrity of your systems.

What you'll learn ✓ Apply your skills to recognize real-world generative AI uses and identify generative AI models and tools for text, code, image, audio, and video. ✓ Explain generative AI prompt engineering concepts, examples, and common tools and learn techniques needed to create effective, impactful prompts. ✓ Implement data engineering processes such as data warehouse schema design, data generation, augmentation and anonymization using generative AI tools ✓ Evaluate real-world case studies showcasing the successful application of generative AI for ETL and data repositories

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.