顺着这个补充一句，我觉得阿布-穆斯塔法（Abu Mostafa）写的《机器学习基石》（Learning from Data）[0] 是该领域极其出色的入门书。它不盲目堆砌数学，但也绝不敷衍略过 [0]:http://www.amazon.com/Learning-From-Data-Yaser-Abu-Mostafa/d...
To tag onto this, I found "Learning from Data" [0] by Abu Mastafa to be a great intro to the field. It's not heavy on the math, but it doesn't gloss over it either [0]:http://www.amazon.com/Learning-From-Data-Yaser-Abu-Mostafa/d...
……来自斯坦福大学：http://infolab.stanford.edu/~ullman/mmds.html 巴伯（Barber）的《贝叶斯推理与机器学习》（Bayesian Reasoning and Machine Learning），网上有免费版：http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=... 阿布-穆斯塔法的《机器学习基石》（Learning from Data），配有加州理工的视频课程：http://work.caltech.edu/telecourse.html 主教（Bishop）的《模式识别与机器学习》（Pattern Recognition and Machine Learning）：http://research.microsoft.com/en-us/um/people/cmbishop/prml/……
…from Stanford. http://infolab.stanford.edu/~ullman/mmds.html Bayesian Reasoning and Machine Learning, by Barber, free available online. http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=... Learning from data, by Abu-Mostafa. It comes with Caltech video lectures: http://work.caltech.edu/telecourse.html Pattern Recognition and Machine Learning, by Bischop http://research.microsoft.com/en-us/um/people/cmbishop/prml/…
加州理工学院亚塞尔·阿布-穆斯塔法教授的《机器学习基石》（Learning from Data）：https://work.caltech.edu/telecourse.html。去年 edX 上还开过这门课。MMDS：《海量数据集挖掘》（Mining Massive Datasets），斯坦福大学教授尤雷·莱斯科韦茨（Jure Leskovec）、阿南德·拉贾拉曼（Anand Rajaraman）、杰夫……
Learning from Data by Caltech Professor Yaser Abu-Mostafa : https://work.caltech.edu/telecourse.html. There was an edX version last year MMDS: Mining Massive Datasets by Stanford professors Jure Leskovec, Anand Rajaraman,Jeff…
……如果有业务领域的经验，那就更好了。现在数据驱动着一切，所以掌握数据分析/机器学习的技能树能让你在任何行业都吃得开。亚塞尔·阿布-穆斯塔法教授的《机器学习基石》（Learning From Data）真是一门宝藏课程，它能帮你洞察机器学习背后的“物理底层”（当然这只是个比喻——ML 本质上大多是向量、矩阵、线性代数这套东西）。课程视频在网上免费……
…experience in the problem domain, even better. Data is driving everything so developing a data analysis/machine learning skillset will put you into any industry you want. Professor Yaser Abu-Mostafa's "Learning From Data" is a gem of a course that helps you see the physics underpinning the learning (metaphorically of course -- ML is mostly vectors, matrices, linear algebra and such). The course videos are online for…
加州理工学院亚塞尔·S·阿布-穆斯塔法教授的课程《机器学习基石》（Learning from Data，http://work.caltech.edu/telecourse），大概是真正理解机器学习运行“物理本质”的最佳入门课了。可以看看亚塞尔教授的一分钟简介：http://www.youtube.com/watch?v=KlP0DpiM7Lw……
Prof Yaser S. Abu-Mostafa's Caltech course "Learning from Data" (http://work.caltech.edu/telecourse) is probably the best introductory course for really understanding the physics of how machine learning works. See Prof's Yaser's 1 min overview: http://www.youtube.com/watch?v=KlP0DpiM7Lw…
这取决于你的数学底子，不过我个人觉得亚塞尔·S·阿布-穆斯塔法的《机器学习基石》（Learning From Data）是最好的，虽说克里斯托弗·M·毕晓普的《模式识别与机器学习》（PRML）才是业内黄金标准。;-)
It depends on your math background, but I've found Yaser S. Abu-Mostafa's Learning From Data the best, although Christopher M. Bishop's Pattern Recognition and Machine Learning is the gold standard. ;-)
顺着这个补充一句，我觉得阿布-穆斯塔法（Abu Mostafa）写的《机器学习基石》（Learning from Data）[0] 是该领域极其出色的入门书。它不盲目堆砌数学，但也绝不敷衍略过 [0]:http://www.amazon.com/Learning-From-Data-Yaser-Abu-Mostafa/d...
To tag onto this, I found "Learning from Data" [0] by Abu Mastafa to be a great intro to the field. It's not heavy on the math, but it doesn't gloss over it either [0]:http://www.amazon.com/Learning-From-Data-Yaser-Abu-Mostafa/d...
……来自斯坦福大学：http://infolab.stanford.edu/~ullman/mmds.html 巴伯（Barber）的《贝叶斯推理与机器学习》（Bayesian Reasoning and Machine Learning），网上有免费版：http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=... 阿布-穆斯塔法的《机器学习基石》（Learning from Data），配有加州理工的视频课程：http://work.caltech.edu/telecourse.html 主教（Bishop）的《模式识别与机器学习》（Pattern Recognition and Machine Learning）：http://research.microsoft.com/en-us/um/people/cmbishop/prml/……
…from Stanford. http://infolab.stanford.edu/~ullman/mmds.html Bayesian Reasoning and Machine Learning, by Barber, free available online. http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=... Learning from data, by Abu-Mostafa. It comes with Caltech video lectures: http://work.caltech.edu/telecourse.html Pattern Recognition and Machine Learning, by Bischop http://research.microsoft.com/en-us/um/people/cmbishop/prml/…
加州理工学院亚塞尔·阿布-穆斯塔法教授的《机器学习基石》（Learning from Data）：https://work.caltech.edu/telecourse.html。去年 edX 上还开过这门课。MMDS：《海量数据集挖掘》（Mining Massive Datasets），斯坦福大学教授尤雷·莱斯科韦茨（Jure Leskovec）、阿南德·拉贾拉曼（Anand Rajaraman）、杰夫……
Learning from Data by Caltech Professor Yaser Abu-Mostafa : https://work.caltech.edu/telecourse.html. There was an edX version last year MMDS: Mining Massive Datasets by Stanford professors Jure Leskovec, Anand Rajaraman,Jeff…
……如果有业务领域的经验，那就更好了。现在数据驱动着一切，所以掌握数据分析/机器学习的技能树能让你在任何行业都吃得开。亚塞尔·阿布-穆斯塔法教授的《机器学习基石》（Learning From Data）真是一门宝藏课程，它能帮你洞察机器学习背后的“物理底层”（当然这只是个比喻——ML 本质上大多是向量、矩阵、线性代数这套东西）。课程视频在网上免费……
…experience in the problem domain, even better. Data is driving everything so developing a data analysis/machine learning skillset will put you into any industry you want. Professor Yaser Abu-Mostafa's "Learning From Data" is a gem of a course that helps you see the physics underpinning the learning (metaphorically of course -- ML is mostly vectors, matrices, linear algebra and such). The course videos are online for…
加州理工学院亚塞尔·S·阿布-穆斯塔法教授的课程《机器学习基石》（Learning from Data，http://work.caltech.edu/telecourse），大概是真正理解机器学习运行“物理本质”的最佳入门课了。可以看看亚塞尔教授的一分钟简介：http://www.youtube.com/watch?v=KlP0DpiM7Lw……
Prof Yaser S. Abu-Mostafa's Caltech course "Learning from Data" (http://work.caltech.edu/telecourse) is probably the best introductory course for really understanding the physics of how machine learning works. See Prof's Yaser's 1 min overview: http://www.youtube.com/watch?v=KlP0DpiM7Lw…
这取决于你的数学底子，不过我个人觉得亚塞尔·S·阿布-穆斯塔法的《机器学习基石》（Learning From Data）是最好的，虽说克里斯托弗·M·毕晓普的《模式识别与机器学习》（PRML）才是业内黄金标准。;-)
It depends on your math background, but I've found Yaser S. Abu-Mostafa's Learning From Data the best, although Christopher M. Bishop's Pattern Recognition and Machine Learning is the gold standard. ;-)