This book will give you a broad overview of what types of learning algorithms are currently most used in the diverse fields of machine learning, and where to watch out when applying them. From our own experience, however, we know that doing the "cool" stuff, that is, using and tweaking machine learning algorithms such as support vector machines, nearest neighbor search, or ensembles thereof, will only consume a tiny fraction of the overall time of a good machine learning expert. Looking at the following typical workflow, we see that most of the time will be spent in rather mundane tasks:

When talking about exploring and understanding the input data, we will need a bit of statistics and basic math. However, while doing that, you will see that those topics that seemed to be so dry in your math class can actually be really exciting when you use them to look at interesting data.

The journey starts when you read in the data. When you have to answer questions such as how to handle invalid or missing values, you will see that this is more an art than a precise science. And a very rewarding one, as doing this part right will open your data to more machine learning algorithms and thus increase the likelihood of success.

With the data being ready in your program's data structures, you will want to get a real feeling of what animal you are working with. Do you have enough data to answer your questions? If not, you might want to think about additional ways to get more of it. Do you even have too much data? Then you probably want to think about how best to extract a sample of it.

Often you will not feed the data directly into your machine learning algorithm. Instead you will find that you can refine parts of the data before training. Many times the machine learning algorithm will reward you with increased performance. You will even find that a simple algorithm with refined data generally outperforms a very sophisticated algorithm with raw data. This part of the machine learning workflow is called feature engineering, and is most of the time a very exciting and rewarding challenge. You will immediately see the results of being creative and intelligent.

Choosing the right learning algorithm, then, is not simply a shootout of the three or four that are in your toolbox (there will be more you will see). It is more a thoughtful process of weighing different performance and functional requirements. Do you need a fast result and are willing to sacrifice quality? Or would you rather spend more time to get the best possible result? Do you have a clear idea of the future data or should you be a bit more conservative on that side?

Finally, measuring the performance is the part where most mistakes are waiting for the aspiring machine learner. There are easy ones, such as testing your approach with the same data on which you have trained. But there are more difficult ones, when you have imbalanced training data. Again, data is the part that determines whether your undertaking will fail or succeed.

We see that only the fourth point is dealing with the fancy algorithms. Nevertheless, we hope that this book will convince you that the other four tasks are not simply chores, but can be equally exciting. Our hope is that by the end of the book, you will have truly fallen in love with data instead of learning algorithms.

To that end, we will not overwhelm you with the theoretical aspects of the diverse ML algorithms, as there are already excellent books in that area (you will find pointers in the Appendix). Instead, we will try to provide an intuition of the underlying approaches in the individual chapters—just enough for you to get the idea and be able to undertake your first steps. Hence, this book is by no means the definitive guide to machine learning. It is more of a starter kit. We hope that it ignites your curiosity enough to keep you eager in trying to learn more and more about this interesting field.

In the rest of this chapter, we will set up and get to know the basic Python libraries NumPy and SciPy and then train our first machine learning using scikit-learn. During that endeavor, we will introduce basic ML concepts that will be used throughout the book. The rest of the chapters will then go into more detail through the five steps described earlier, highlighting different aspects of machine learning in Python using diverse application scenarios.