嗨，我是机器学习博士——你可以从巩固一些基础开始。像 Thom M. Mitchell 的《Machine Learning》这样的书是个合理的起点。另外，在应用 ML 方面，你可以快速过一遍……
Hi, machine learning PhD here - one way you can start is by brushing up on some fundamentals. A book such as Machine Learning, by Thom M. Mitchell is a reasonable start. Also, in terms of applying ML, you could scoot through…
我最喜欢的机器学习入门教材是 Tom Mitchell 的那本：http://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/007... 不过 Bishop 那本是最流行的：http://www.amazon.com/gp/product/0387310738/ref=pd_rvi_gw_2/...
My favorite introductory textbook on machine learning is the Tom Mitchell book: http://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/007... The Bishop book is the most popular though: http://www.amazon.com/gp/product/0387310738/ref=pd_rvi_gw_2/...
……非常平易近人，也介绍了一些很有意思的话题（这样你就不会写正则去匹配 HTML 了，还会学到 P vs NP 是什么意思）。更偏实践方面，我认为机器学习必读的是 Tom Mitchell 的《Machine Learning》。另一本据我所知更容易消化的书是《Data Mining: Practical Machine Learning Tools and Techniques》。
…is very approachable and presents some very interesting topics (so you won't write a regex for matching HTML and will learn what P vs NP means). On a more practical side, I think that a must read for machine learning is Tom Mitchell - Machine Learning . Another book that from what I've heard is easier to digest is Data Mining: Practical Machine Learning Tools and Techniques.
This is kind of a masters degree course i created for myself to get knowledge of Machine Learning from bottoms up First, you need a strong mathematical base. Otherwise, you can copy paste an algorithm or use an API but you will not get any idea of what is happening inside Following concepts are very…
嗨，我是机器学习博士——你可以从巩固一些基础开始。像 Thom M. Mitchell 的《Machine Learning》这样的书是个合理的起点。另外，在应用 ML 方面，你可以快速过一遍……
Hi, machine learning PhD here - one way you can start is by brushing up on some fundamentals. A book such as Machine Learning, by Thom M. Mitchell is a reasonable start. Also, in terms of applying ML, you could scoot through…
想看严肃学术书就选 Tom Mitchell 的《Machine Learning》。想看 Python 实战进阶就选 Toby Segaran 的《Programming Collective Intelligence》。
Machine Learning by Tom Mitchell for a serious academic book. Programming Collective Intelligence by Toby Segaran for a practical approach in Python.
我最喜欢的机器学习入门教材是 Tom Mitchell 的那本：http://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/007... 不过 Bishop 那本是最流行的：http://www.amazon.com/gp/product/0387310738/ref=pd_rvi_gw_2/...
My favorite introductory textbook on machine learning is the Tom Mitchell book: http://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/007... The Bishop book is the most popular though: http://www.amazon.com/gp/product/0387310738/ref=pd_rvi_gw_2/...
……非常平易近人，也介绍了一些很有意思的话题（这样你就不会写正则去匹配 HTML 了，还会学到 P vs NP 是什么意思）。更偏实践方面，我认为机器学习必读的是 Tom Mitchell 的《Machine Learning》。另一本据我所知更容易消化的书是《Data Mining: Practical Machine Learning Tools and Techniques》。
…is very approachable and presents some very interesting topics (so you won't write a regex for matching HTML and will learn what P vs NP means). On a more practical side, I think that a must read for machine learning is Tom Mitchell - Machine Learning . Another book that from what I've heard is easier to digest is Data Mining: Practical Machine Learning Tools and Techniques.
这算是我给自己创建的一个硕士课程，为了从头开始建立机器学习知识。首先，你需要扎实的数学基础。否则，你可以复制粘贴一个算法或使用 API，但你不会理解内部发生了什么。以下概念非常……
This is kind of a masters degree course i created for myself to get knowledge of Machine Learning from bottoms up First, you need a strong mathematical base. Otherwise, you can copy paste an algorithm or use an API but you will not get any idea of what is happening inside Following concepts are very…
我认为很多（如果不是大多数）主题并没有这样一个显而易见的单一权威来源。机器学习：Tom M. Mitchell
I think many (if not most) topics don't have such an obvious single authorative source. Machine Learning: Tom M. Mitchell