I've personally found Christopher Bishop's Pattern recognition and Machine learning to be a nice introduction. 

http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738

I'd also recommend taking a look a Andrew Ng's Machine Learning course on Coursera - his explanations are excellent. 

https://www.coursera.org/learn/machine-learning/

Hi, although I am not an expert, but I can recommand some books that I have read.

1

Pattern recognition and machinel learning, http://book.douban.com/subject/2061116/

it is a detailed book， but there existing some formula may be difficult to people who don't have some much mathmatic background.

2

Statistical Pattern Recognition 

It is a handbook for statistical learner, our teacher used it as our textbook.

3

Graphical Models, Exponential Families, and Variational Inference

As an excellent part of machine learning, graphical model is very interesting. I am now reading it.

4

some book about R 

an introduction to statistical learning.

http://vdisk.weibo.com/s/yVFSlzgEVtvZ6(you can download from here)

bayssian networkin R

grahpical model in R

these books above are brief in formula and calculate, but it teach the reader how to use software R to solve the problems, putting the theory into practice quickly.

gook luck!

Thank you Chen

Hi, here are some good resources to start from (from my experience).

Python oriented:

- Programming Collective Intelligence http://shop.oreilly.com/product/9780596529321.do

Very popular among beginners with real-life examples (data)

Many Python examples

- Machine Learning an Algorithmic Perspective

http://www.amazon.com/Machine-Learning-Algorithmic-Perspective-Recognition/dp/1420067184

Mainly focused on algorithms

Not so much math and many Python examples

- Machine Learning in Action

http://www.amazon.com/Machine-Learning-Action-Peter-Harrington/dp/1617290181

Similar to the latter; algorithm hands-on

Many Python examples

Weka (platform) oriented:

- Data Mining: Practical Machine Learning Tools and Techniques

http://www.cs.waikato.ac.nz/ml/weka/book.html

Very good introduction on ML

It is a companion book for the Weka platform

I would also look into:

- Tom Mitchell's book - Machine Learning (1997) - and online material

http://www.cs.cmu.edu/~tom/

- Christopher Bishop's - Pattern Recognition and Machine Learning (2007)

http://www.springer.com/gp/book/9780387310732

- Jason Brownlee's - Machine Learning Mastery web page.

http://machinelearningmastery.com

Good luck!

I've personally found Christopher Bishop's Pattern recognition and Machine learning to be a nice introduction. 

http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738

I'd also recommend taking a look a Andrew Ng's Machine Learning course on Coursera - his explanations are excellent. 

https://www.coursera.org/learn/machine-learning/

the best book is: "Machine Learning" by Tom Mitchell

www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlbook.html

Hey, beyond the recent answers and especially the hint to Bishop's "Pattern Recognitin and Machine Learning" and Ng's lectures, I'd like to add Haykin's "Neural Networks and Learning Machines" as well as Murphey's "Machine Learning: A Probabilistic Perspective". There is also a new deep learning textbook in preparation by Bengio (http://www.iro.umontreal.ca/~bengioy/dlbook/)

Have fun!

Also this one

S. Haykin, “Neural Networks and Learning Machines”, 3rd Edition, Pearson Education, 2008.

http://www.amazon.com/gp/product/0131471392/ref=as_li_ss_tl?ie=UTF8&tag=internetbas01-20&linkCode=as2&camp=217145&creative=399369&creativeASIN=0131471392

I would recommend:

Alpaydin, E., 2004, “Introduction to Machine Learning”, Cambridge: MIT Press.

It is a very good book and easy to understand. I also strongly recommend  Andrew Ng machine learning course on Coursear: