We try to convey every idea necessary to reproduce the steps throughout this book. Nevertheless, there will be situations when you might get stuck. The reasons might range from simple typos over odd combinations of package versions to problems in understanding.
In such a situation, 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://metaoptimize.com/qa – This Q&A site is laser-focused on machine learning topics. For almost every question, it contains above-average answers from machine learning experts. Even if you don't have any questions, it is a good habit to check it out every now and then and read through some of the questions and answers.
- http://stats.stackexchange.com – This Q&A site, named Cross Validated, is similar to MetaOptimized, but focuses more on statistics problems.
- http://stackoverflow.com – This Q&A site is similar to the previous ones, 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 (SciPy and Matplotlib).
#machinelearningon Freenode – This IRC channel is focused on machine learning topics. It is a small but very active and helpful community of machine learning experts.- http://www.TwoToReal.com – This is an instant Q&A site written by us, the authors, to support you in topics that don't fit in any of the above buckets. If you post your question, we will get an instant message; if any of us are online, we will be drawn into a chat with you.
As stated at the beginning, this book tries to help you get started quickly on your machine learning journey. We therefore 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 does not.
The only blog we want to highlight right here is http://blog.kaggle.com, the blog of the Kaggle company, which is carrying out machine learning competitions (more links are provided in Appendix A, Where to Learn More about Machine Learning). 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. If you don't read anything else, fine; but this is a must.