This second course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the practical aspects of managing machine learning projects. The course walks through the keys steps of a ML project from how to identify good opportunities for ML through data collection, model building, deployment, and monitoring and maintenance of production systems. Participants will learn about the data science process and how to apply the process to organize ML efforts, as well as the key considerations and decisions in designing ML systems.
At the conclusion of this course, you should be able to:
1) Identify opportunities to apply ML to solve problems for users
2) Apply the data science process to organize ML projects
3) Evaluate the key technology decisions to make in ML system design
4) Lead ML projects from ideation through production using best practices
In this module we will discuss how to identify problems worth solving, how to determine whether ML is a good fit as part of the solution, and how to validate solution concepts. We will also learn why heuristics are useful in modeling projects and the advantages and disadvantages of ML relative to h…
In this module we will focus on the CRISP-DM data science process and how it can be used to organize ML projects. We will begin by understanding what is unique about ML project relative to normal software projects, and then discuss approaches to manage the inherent risks of ML projects. We will als…
In this module we will explore the key data-related issues that arise in ML projects. Data is the foundation of successful machine learning, and gathering data of sufficient quantity and quality with the right set of attributes is the key to a successful project. We will discuss the key considerati…
In this module we will discuss the key decisions to make in designing ML systems, such as cloud vs. edge and online vs. batch, and compare the benefits of each type of system. We will then discuss the primary technology decisions to make in a ML project and introduce the common tools and technologi…
The final module in the course focuses on identifying and mitigating the key issues which ML models experience once they are in production. We will discuss how to set up a robust ML system monitoring capability and define a model maintenance plan to maintain high performance of a production model.…
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Duke University is in Tier 2: excellent universities and the companies that build the technology of our institution ranking (86/100).
Instructor: Prof. Gary Gensler View the complete course: https://ocw.mit.edu/15-S12F18 This course is for students wishing to explore blockchain technology’s potential use - by entrepreneurs & incumbents - to change the world of money and finance. License: Creative Commons BY-NC-SA More information…
This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performa…
This is CS50P, CS50's Introduction to Programming with Python. Register for free at https://cs50.edx.org/python. Slides and source code at https://cs50.harvard.edu/python. Playlist at https://www.youtube.com/playlist?list=PLhQjrBD2T3817j24-GogXmWqO5Q5vYy0V. An introduction to programming using a la…
This is CS50, Harvard University's introduction to the intellectual enterprises of computer science and the art of programming, for concentrators and non-concentrators alike, with or without prior programming experience. (Two thirds of CS50 students have never taken CS before.) This course teaches…
Instructor: Ana Bell View the complete course: https://ocw.mit.edu/courses/6-100l-introduction-to-cs-and-programming-using-python-fall-2022/ *Note: Lectures 6, 10 have been updated! This subject is aimed at students with little to no programming experience. It aims to provide students with an under…
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