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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-learning-foundations-for-product-managers
课程评论：没有评论
课程名称：产品经理的机器学习基础 课程概述：本课程是杜克大学普拉特工程学院人工智能产品管理专业的第一门课程，旨在帮助学员建立机器学习的基础理解，包括机器学习的定义、工作原理以及应用场景。为了成功管理AI团队或产品，并与数据科学家、软件工程师和客户进行有效协作，需要掌握机器学习技术的基础知识。本课程提供非编码的机器学习入门，重点关注模型开发流程、机器学习模型的评估与解读，以及常见机器学习和深度学习算法的直觉理解。课程最后将通过一个实践项目，让学员有机会在一个简单的实际问题上训练和优化机器学习模型。 在课程结束时，学员应该能够： 1) 解释机器学习的工作原理及其类型 2) 描述建模中的挑战及应对策略 3) 识别用于常见机器学习任务的主要算法及其应用场景 4) 解释深度学习及其相对于其他机器学习形式的优势与挑战 5) 实施评估和解读机器学习模型的最佳实践 课程大纲： 1. 什么是机器学习：介绍机器学习的概念，构建数据和模型的词汇，理解不同类型的机器学习，并讨论机器学习的能力和局限性。 2. 建模流程：讨论构建机器学习模型的关键步骤，了解模型复杂性的来源及其对性能的影响，探讨比较不同模型的策略以选择最佳模型。 3. 模型评估与解读：定义AI项目的适当结果和输出指标，讨论评估回归和分类模型的关键指标，并分析机器学习项目中常见的误差来源和解决策略。 4. 线性模型：探索线性模型在回归和分类中的应用，从线性回归开始，讨论正则化如何提升模型表现，接着介绍适用于二分类和多分类问题的逻辑回归模型。 5. 树模型、集成模型与聚类：讨论树模型在建模复杂非线性问题中的价值，介绍创建集成模型的方法与优势，最后探讨无监督学习中的聚类方法，特别是K-Means聚类。 6. 深度学习与课程项目：聚焦于深度学习，即多层神经网络的使用，理解其背后的直觉和数学原理，讨论深度学习在计算机视觉和自然语言处理中的常见应用。课程以实践项目收尾，学员将应用所学的建模流程和最佳实践，创建自己的机器学习模型。
Name:What is Machine Learning
Description: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.
Name:The Modeling Process
Description: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.
Name:Evaluating & Interpreting Models
Description: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.
Name:Linear Models
Description: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.
Name:Trees, Ensemble Models and Clustering
Description:We will begin this model with a discussion of tree models and their value in modeling compex non-linear problems. We will then introduce the method of creating ensemble models and their benefits. We will wrap this module up by switching gears to unsupervised learning and discussing clustering and the popular K-Means clustering approach.
Name:Deep Learning & Course Project
Description:Our final module in this course will focus on a hot area of machine learning called deep learning, or the use of multi-layer neural networks. We will develop an understanding of the intuition and key mathematical principles behind how neural networks work. We will then discuss common applications of deep learning in computer vision and natural language processing. We will wrap up the course with our course project, where you will have an opportunity to apply the modeling process and best practices you have learned to create your own machine learning model.
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. At the conclusion of this course, you should be able to: 1) Explain how machine learning works and the types of machine learning 2) Describe the challenges of modeling and strategies to overcome them 3) Identify the primary algorithms used for common ML tasks and their use cases 4) Explain deep learning and its strengths and challenges relative to other forms of machine learning 5) Implement best practices in evaluating and interpreting ML models
