Summary

In this chapter, which forms the basis for the rest of the chapters of this book, we covered the basics of Machine learning and the landscape of Machine learning semantics. We started by defining Machine learning in simple terms and introduced Machine learning jargon or the commonly used terms.

There are many competing and complementing fields of Machine learning. We have thoroughly explained the similarities, dissimilarities, and the relationship of Machine learning with fields such as artificial intelligence, data mining, data science, and statistics. Overall, all these fields are very similar and have overlapping goals. In most cases, the practitioners of these fields were different. Even in terms of the tools being used, there were many common points.

We have also looked at some of the latest and best-of-breed tools that can be employed in Machine learning. Some of these tools will be demonstrated in the chapters using practical examples.

In the next chapter, we will cover a unique aspect of Machine learning that has pretty much changed the way Machine learning implementations have been looked at. We will explore how the big data, or large dataset, aspect of Machine learning has impacted the choice of tools and implementation approaches.