Welcome to the AI Product Management specialization. My name is Jon Reifschneider. I'm the Director of the Master of Engineering Program and AI for product innovation at Duke University, and I'll be your instructor. This specialization contains three courses, each focusing on a different aspect of working with AI products. The first course, machine learning foundations for product managers, provides an intuitive introduction to the theory behind machine learning. We'll focus on building your intuition for what machine learning is and how it works. We'll cover several of the key algorithms used, including both the classical algorithms as well as deep learning, and we'll discuss the process for building, training, evaluating, and interpreting machine learning models. The second course in this specialization, managing machine learning projects, focuses on how to organize machine learning development. We'll discuss how to implement the data science process to organize machine learning projects, we'll talk about team structure, how to overcome common pitfalls that make so many machine learning projects unsuccessful, and we'll cover important topics such as machine learning system design and model deployment and maintenance. In the third course in this specialization, human factors in AI, we'll focus on how we as humans interact with AI systems. We'll discuss important aspects of user experience design in building AI systems, and we'll dive into the important ethical and legal and privacy considerations in using AI within products. Companies within every industry today are either already using AI, or planning to embed AI within their products. This is creating huge demand for workers who are comfortable in building AI-based products and managing such products. This includes not only data scientists and software engineers, but workers across other functions such as product management, customer service, sales, engineering team management, and even executives. Yet building successful AI products is hard, the failure rate of machine learning projects is very high. There's much more complexity, more uncertainty, and a much higher degree of technical risk relative to normal software projects. This specialization provides two key elements to help improve the success rate of machine learning projects. The first is an intuitive introduction to machine learning, how it works, how to apply it to solve problems, the most popular machine learning algorithms, and the process of building and training models. The second is a set of best practices in building machine learning products. We'll discuss how to managed projects using the data science process, how to design machine learning systems, how to deploy and manage models in production, and we'll talk about the important human considerations such as user experience design, privacy, and ethics. We have three main learning objectives for this specialization. At the conclusion of this specialization, you should be able to communicate in the language of data science and machine learning so that everybody on the team is speaking in the same language. You should be capable of leading AI and machine learning development projects, applying the best practices we discussed throughout the courses, and you should be capable of considering and making decisions on how to integrate the key human factors in designing AI product experiences.
