id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
www-coursera-org-230		Machine Learning by Stanford University | Coursera			.html	text/html	3519	627	58	Machine learning is the science of getting computers to act without being explicitly programmed. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning. This course includes programming assignments designed to help you understand how to implement the learning algorithms in practice. In this module, we discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data. In this module, we introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets. In this module, we discuss how to apply the machine learning algorithms with large datasets. AI and Machine Learning Certificate	./cache/www-coursera-org-230.html	./txt/www-coursera-org-230.txt