Chapter 1: Understanding the AI/ML landscape

In this opening chapter, we'll give you a little appreciation and context to the why behind AI and machine learning (ML). The only data we have comes from the past, and using that will help us predict the future. We'll take a look at the massive amount of data that is coming into the world today and try to get a sense of the scale of what we have to work with.

The main goal of any type of software or algorithm is to solve business and real-world problems, so we'll also take a look at how the applications take shape. If we use a food analogy, data would be the ingredients, the algorithm would be the chef, and the meal created would be the model. You'll learn about the most commonly used types of models within the broader landscape and how to know what to use.

There are a huge number of tools that you could use as a data scientist, and so we will also touch on how you can use solutions such as those provided by Anaconda to be able to do the actual work you want to and be able to take action as your models grow stale (which they will). By the end of this chapter, you'll have an understanding of the value and landscape of AI and be able to jumpstart any project that you want to build.

AI is the most exciting technology of our age and, throughout this first chapter, these topics will give you the solid foundation that we'll build upon through the rest of the book. These are all key concepts that will be commonplace in your day-to-day journey, and which you'll find to be invaluable in accomplishing what you need to.

In this chapter, we're going to cover the following main topics:

• Understanding the current state of AI and ML
• Understanding the massive generation of new data
• How to create business value with AI
• Understanding the main types of ML models
• Dealing with out-of-date models
• Installing packages with Anaconda