"The nice thing about having a brain is that one can learn, that ignorance can be supplanted by knowledge, and that small bits of knowledge can gradually pile up into substantial heaps."

--Douglas Hofstadter

Machine learning is often referred to as the part of artificial intelligence that actually works. Its aim is to find a function based on an existing set of data (training set) in order to predict outcomes of a previously unseen dataset (test set) with the highest possible correctness. This occurs either in the form of labels and classes (classification problems) or in the form of a continuous value (regression problems). Tangible examples of machine learning in real-life applications range from predicting future stock prices to classifying the gender of an author from a set of documents. Throughout this book, the most important machine learning concepts, together with methods suitable for larger datasets, will be made clear to the reader, thanks to practical examples in Python. We will look at supervised learning (classification & regression), as well as unsupervised learning (such as Principal Component Analysis (PCA), clustering, and topic modeling) that have been found to be applicable to larger datasets.

Large IT corporations such as Google, Facebook, and Uber have generated a lot of buzz by claiming that they successfully applied such machine learning methods at a large scale. With the onset and availability of big data, the demand for scalable machine learning solutions has grown exponentially and many other companies and individuals have started aspiring to ripe the fruits of hidden correlations in big datasets. Unfortunately, most learning algorithms don't scale well, straining CPUs and memory either on a desktop computer or on a larger computing cluster. During these times, even if big data has passed the peak of hype, scalable machine learning solutions are not plentiful.

Frankly, we still need to work around a lot of bottlenecks even with datasets we would hardly categorize as big data (think of datasets up to 2GB or even smaller). The mission of this book is to provide methods (and sometimes unconventional ones) to apply the most powerful open source machine learning methods at a larger scale, without the need for expensive enterprise solutions or large computing clusters. Throughout this book, we will use Python and some other readily available solutions that integrate well in scalable machine learning pipelines. Reading the book is a journey that will redefine what you knew about machine learning, setting you on the starting blocks of real big data analysis.