Introduction

Machine learning (ML), without a doubt, is one of the most relevant technologies nowadays as it aims to convert information (data) into knowledge that can be used to make informed decisions. In this chapter, you will learn about the different applications of ML in today's world, as well as the role that data plays. This will be the starting point for introducing different data problems throughout this book that you will be able to solve using scikit-learn.

Scikit-learn is a well-documented and easy-to-use library that facilitates the application of ML algorithms by using simple methods, which ultimately enables beginners to model data without the need for deep knowledge of the math behind the algorithms. Additionally, thanks to the ease of use of this library, it allows the user to implement different approximations (that is, create different models) for a data problem. Moreover, by removing the task of coding the algorithm, scikit-learn allows teams to focus their attention on analyzing the results of the model to arrive at crucial conclusions.

Spotify, a world-leading company in the field of music streaming, uses scikit-learn because it allows them to implement multiple models for a data problem, which are then easily connected to their existing development. This process improves the process of arriving at a useful model, while allowing the company to plug them into their current app with little effort.

On the other hand, booking.com uses scikit-learn due to the wide variety of algorithms that the library offers, which allows them to fulfill the different data analysis tasks that the company relies on, such as building recommendation engines, detecting fraudulent activities, and managing the customer service team.

Considering the preceding points, this chapter also explains scikit-learn and its main uses and advantages, and then moves on to provide a brief explanation of the scikit-learn Application Programming Interface (API) syntax and features. Additionally, the process of representing, visualizing, and normalizing data will be shown. The aforementioned information will help us to understand the different steps that need to be taken to develop a ML model.

In the following chapters in this book, you will explore the main ML algorithms that can be used to solve real-life data problems. You will also learn about different techniques that you can use to measure the performance of your algorithms and how to improve them accordingly. Finally, you will explore how to make use of a trained model by saving it, loading it, and creating APIs.