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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning
课程评论：没有评论
课程名称：无监督学习、推荐系统与强化学习 课程概述：在机器学习专业化的第三个课程中，您将学习： - 无监督学习技术，包括聚类和异常检测。 - 使用协同过滤方法和基于内容的深度学习方法构建推荐系统。 - 构建深度强化学习模型。 该机器学习专业化是由DeepLearning.AI与斯坦福在线合作创建的基础性在线程序。在这个适合初学者的课程中，您将学习机器学习的基础知识，并了解如何应用这些技术来构建实际的人工智能应用程序。 本专业化课程由人工智能领域的先驱Andrew Ng教授授课，他曾在斯坦福大学进行重要研究，并在Google Brain、百度及Landing.AI开展开创性工作，推动人工智能的发展。 该3门课程的专业化是Andrew开创性机器学习课程的更新和扩展版，自2012年推出以来，评分达到4.9分，吸引超过480万学习者。 课程内容广泛地介绍了现代机器学习，包括监督学习（多元线性回归、逻辑回归、神经网络及决策树）、无监督学习（聚类、降维、推荐系统），以及硅谷在人工智能和机器学习创新中的最佳实践（评估和调整模型、数据中心方法以改善性能等）。 完成该专业化课程后，您将掌握关键概念，获得迅速有效应用机器学习于复杂现实问题的实用知识。如果您希望进入人工智能领域或在机器学习中建立职业生涯，这个新的机器学习专业化课程是最佳起点。 课程大纲： - 名称：无监督学习 描述：本周，您将学习两个关键的无监督学习算法：聚类和异常检测。 - 名称：推荐系统 描述：将学习如何构建推荐系统。 - 名称：强化学习 描述：本周，您将学习强化学习，并构建深度Q学习神经网络，以将虚拟登月舱安全送达火星！
Name:Unsupervised learning
Description:This week, you will learn two key unsupervised learning algorithms: clustering and anomaly detection
Name:Recommender systems
Description:
Name:Reinforcement learning
Description:This week, you will learn about reinforcement learning, and build a deep Q-learning neural network in order to land a virtual lunar lander on Mars!
In the third course of the Machine Learning Specialization, you will: • Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection. • Build recommender systems with a collaborative filtering approach and a content-based deep learning method. • Build a deep reinforcement learning model. The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
