Introduction to Machine Learning

Machine learning is everywhere. When you book a flight ticket, an algorithm decides the price you are going to pay for it. When you apply for a loan, machine learning may decide whether you are going to get it or not. When you scroll through your Facebook timeline, it picks which advertisements to show to you. Machine learning also plays a big role in your Google search results. It organizes your email's inbox and filters out spam, it goes through your resumé before recruiters when you apply for a job, and, more recently, it has also started to play the role of your personal assistant in the form of Siri and other virtual assistants.

In this book, we will learn about the theory and practice of machine learning. We will understand when and how to apply it. To get started, we will look at a high-level introduction to how machine learning works. You will then be able to differentiate between the different machine learning paradigms and know when to use each of them. Then, you'll be taken through the model development life cycle and the different steps practitioners take to solve problems. Finally, we will introduce you to scikit-learn, and learn why it is the de facto tool for many practitioners.

Here is a list of the topics that will be covered in this first chapter:

• Understanding machine learning
• The model development life cycle
• Introduction to scikit-learn
• Installing the packages you need