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 what to watch out for 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 searches, or ensembles thereof—will only consume a tiny fraction of the overall time that a good machine learning expert will spend doing the same thing. Looking at the following typical workflow, we can see that most of the time will be spent on rather mundane tasks:

• Reading the data and cleaning it
• Exploring and understanding the input data
• Analyzing how to best present the data to the learning algorithm
• Choosing the right model and learning algorithm
• Measuring the performance correctly

When talking about exploring and understanding the input data, we will need to use a bit of statistics and basic math. However, while doing this, you will see that those topics that seemed 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 do I handle invalid or missing values?", you will see that this is more an art than a precise science, and a very rewarding art, as doing this part correctly will open your data to more machine learning algorithms and hence increase the likelihood of success.

With the data ready and waiting in your program's data structures, you will want to get a real feeling for 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. Perhaps you even have too much data. In that case, you probably want to think about how to best 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. Oftentimes, 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, most of the time, it is a very exciting and rewarding challenge. You will immediately see the results of your previous creative and intelligent efforts.

Choosing the right learning algorithm, then, is not simply a shoot-out 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 future data, or should you be a bit more conservative on that side?

Finally, measuring performance is the part of the process that has the most potential pitfalls for the aspiring machine learner. There are easy mistakes to avoid, such as testing your approach with the same data on which you have trained. But there are more difficult ones as well, such as using imbalanced training data. Again, the data is the part that determines whether your undertaking will fail or succeed.

We see that only the fourth point deals 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 machine learning algorithms, as there are already excellent books in that area (you will find pointers in the appendix). Instead, we will try to provide you with an understanding of the underlying approaches in the individual chapters—just enough for you to get an 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 of NumPy and SciPy, and then train our first machine learning algorithm using scikit-learn. During this, we will introduce basic machine learning concepts that will be used throughout the book. The rest of the chapters will then go into more detail about the five steps described earlier, highlighting different aspects of machine learning in Python using diverse application scenarios.