We have tried to convey every idea necessary to reproduce the steps throughout this book. Nevertheless, there will be situations where you are stuck. The reasons might range from simple typos over odd combinations of package versions to problems in understanding.

There are many different ways to get help. Most likely, your problem will already have been raised and solved in the following excellent Q&A sites:

• http://stats.stackexchange.com: This Q&A site is named Cross Validated, similar to MetaOptimize, but is focused more on statistical problems.
• http://stackoverflow.com: This Q&A site is much like the previous one, but with a broader focus on general programming topics. It contains, for example, more questions on some of the packages that we will use in this book, such as SciPy or Matplotlib.
• https://freenode.net/: This is the IRC channel focused on machine learning topics. It is a small but very active and helpful community of machine learning experts.

As stated at the beginning, this book is intended to help you get started quickly on your machine learning journey. Therefore, we highly encourage you to build up your own list of machine learning-related blogs and check them out regularly. This is the best way to get to know what works and what doesn't.

The only blog we want to highlight right here (though there there are more in the appendix) is http://blog.kaggle.com, the blog of the company, Kaggle, which hosts machine learning competitions. Typically, they encourage the winners of the competitions to write down how they approached the competition, what strategies did not work, and how they arrived at the winning strategy. Even if you don't read anything else, this is a must.