数据科学正为我们带来让人振奋的新机遇，对于那些希望利用Azure的人工智能和机器学习能力的微软开发人员来说，更是如此。本课程介绍了Azure机器学习的基本概念。了解受监督学习、非受监督学习和强化学习之间的区别，以及影响任何数据科学项目成功的重要因素:使用的数据的质量、提出的问题和做出的预测。讲师还回顾了一些与Azure相关的机器学习算法——集群、异常检测、分类和回归等知识。

Data science is opening up exciting new opportunities, especially for Microsoft developers who want to take advantage of the artificial intelligence and machine learning capabilities of Azure. This introductory course provides an overview of the basic concepts underlying Azure Machine Learning. Learn the difference between supervised, unsupervised, and reinforcement learning and important factors that impact the success of any data science project: the quality of the data you use, the questions you ask, and the predictions you make. Instructor Sahil Malik also reviews some of the machine learning algorithms—clustering, anomaly detection, classification, and regression—that are most relevant to Azure.