In this first course of the AI Product Management Specialization offered by Duke University's Pratt School of Engineering, you will build a foundational understanding of what machine learning is, how it works and when and why it is applied. To successfully manage an AI team or product and work collaboratively with data scientists, software engineers, and customers you need to understand the basics of machine learning technology. This course provides a non-coding introduction to machine learning, with focus on the process of developing models, ML model evaluation and interpretation, and the intuition behind common ML and deep learning algorithms. The course will conclude with a hands-on project in which you will have a chance to train and optimize a machine learning model on a simple real-world problem.
提供：
AI Product Management専門講座デューク大学（Duke University）このコースについて
No prior knowledge of machine learning or programming experience required
習得するスキル
- Data Science
- Artificial Neural Network
- Machine Learning
- Predictive Analytics
- Modeling
No prior knowledge of machine learning or programming experience required
提供：
デューク大学（Duke University）
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
シラバス - 本コースの学習内容
What is Machine Learning
In this module we will be introduced to what machine learning is and does. We will build the necessary vocabulary for working with data and models and develop an understanding of the different types of machine learning. We will conclude with a critical discussion of what machine learning can do well and cannot (or should not) do.
The Modeling Process
In this module we will discuss the key steps in the process of building machine learning models. We will learn about the sources of model complexity and how complexity impacts a model's performance. We will wrap up with a discussion of strategies for comparing different models to select the optimal model for production.
Evaluating & Interpreting Models
In this module we will learn how to define appropriate outcome and output metrics for AI projects. We will then discuss key metrics for evaluating regression and classification models and how to select one for use. We will wrap up with a discussion of common sources of error in machine learning projects and how to troubleshoot poor performance.
Linear Models
In this module we will explore the use of linear models for regression and classification. We will begin with introducing linear regression and continue with a discussion on how to make linear regression work better through regularization. We will then switch to classification and introduce the logistic regression model for both binary and multi-class classification problems.
レビュー
- 5 stars83.33%
- 4 stars8.33%
- 3 stars1.66%
- 1 star6.66%
MACHINE LEARNING FOUNDATIONS FOR PRODUCT MANAGERS からの人気レビュー
Very good courses that clearly and precisely covered the foundation concepts for machine leaning!
A very clear introduction to the 'types' of Artificial Intelligence and other necessary concepts required in dealing with AI.
The training provides a good overview of ML concepts. At the same time pre-project data quality review and initial data analysis could have a more extensive coverage from my point of view
A very good introduction to ML Jon Reifschneider explains very well the topics with real-world experience -based on this professional experience.
AI Product Management専門講座について
Organizations in every industry are accelerating their use of artificial intelligence and machine learning to create innovative new products and systems. This requires professionals across a range of functions, not just strictly within the data science and data engineering teams, to understand when and how AI can be applied, to speak the language of data and analytics, and to be capable of working in cross-functional teams on machine learning projects.
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