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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-learning
课程评论：没有评论
课程名称：机器学习 课程概述：机器学习是让计算机无需明确编程即可行动的科学。在过去十年中，机器学习的发展带来了自动驾驶汽车、实用的语音识别、有效的网络搜索以及对人类基因组的深刻理解。如今，机器学习无处不在，你可能每天不知不觉中使用它数十次。许多研究人员认为，机器学习是朝着人类水平人工智能取得进展的最佳途径。在本课程中，你将学习到最有效的机器学习技术，并在实践中应用这些技术，帮助你快速、高效地解决新问题。更重要的是，你将了解学习的理论基础，并掌握实际应用这些技术的方法。此外，你还将学习到硅谷在机器学习和人工智能领域的创新最佳实践。 本课程提供了机器学习、数据挖掘和统计模式识别的广泛介绍。涉及的主题包括： (i) 监督学习（参数/非参数算法、支持向量机、核函数、神经网络）。 (ii) 无监督学习（聚类、降维、推荐系统、深度学习）。 (iii) 机器学习的最佳实践（偏差/方差理论；机器学习和人工智能中的创新过程）。 课程还将参考众多案例研究和应用，使你学习如何将学习算法应用于智能机器人（感知、控制）、文本理解（网络搜索、反垃圾邮件）、计算机视觉、医疗信息学、音频、数据库挖掘及其他领域。 课程大纲： - 第1周：机器学习简介 欢迎来到机器学习专业课程！你将加入数百万其他学习者的行列，他们已经完成了这门课程，帮助他们探索机器学习的激动人心的世界。 - 第2周：多输入变量的回归 本周，你将扩展线性回归以处理多个输入特征。你还将学习一些改进模型训练和性能的方法，如向量化、特征缩放、特征工程和多项式回归。周末你将最终在代码中实践线性回归。 - 第3周：分类 本周，你将学习监督学习的另一种类型——分类。你将学习如何使用逻辑回归模型预测类别。你将了解过拟合的问题，并学习使用正则化方法来处理该问题。本周结束时，你将有机会实践实施带正则化的逻辑回归。
Name:Week 1: Introduction to Machine Learning
Description:Welcome to the Machine Learning Specialization! You're joining millions of others who have taken either this or the original course, which led to the founding of Coursera, and has helped millions of other learners, like you, take a look at the exciting world of machine learning!
Name:Week 2: Regression with multiple input variables
Description:This week, you'll extend linear regression to handle multiple input features. You'll also learn some methods for improving your model's training and performance, such as vectorization, feature scaling, feature engineering and polynomial regression. At the end of the week, you'll get to practice implementing linear regression in code.
Name:Week 3: Classification
Description:This week, you'll learn the other type of supervised learning, classification. You'll learn how to predict categories using the logistic regression model. You'll learn about the problem of overfitting, and how to handle this problem with a method called regularization. You'll get to practice implementing logistic regression with regularization at the end of this week!
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
