Introduction to Machine Learning Using Python

The last chapter introduced you to the world of machine learning (ML). In this chapter, we will develop the ML foundations that are required for building and using Automated ML (AutoML) platforms. It is not always clear how ML is best applied or what it takes to implement it. However, ML tools are getting more straightforward to use, and AutoML platforms are making it more accessible to a broader audience. In the future there will undoubtedly be a higher collaboration between man and machine. 

The future of ML may require people to prepare data for its consumption and identify use cases for implementation. More importantly, people are needed to interpret the results and audit the ML system—whether they are following the right and best approaches to solving a problem. The future looks pretty amazing, but we need to build that future; that's what we are going to do in this book. In this chapter, we will walk you through the following topics:

• Machine learning process and its different types
• Supervised learning—regression and classification
• Unsupervised learning—clustering
• Ensembles—bagging, boosting, and stacking
• Inferring tasks based on data
• Task-specific evaluation metrics

We understand that a single chapter is not enough to learn and practice ML. There are already many excellent books and materials available on ML where you can find a detailed discussion on each of the mentioned topics. You can see some recommendations in the Other Books You May Enjoy section of our Back Matter. The objective of this chapter is to provide you with an overview of the different ML techniques and discuss some of its essential aspects that are necessary to work on the subsequent chapters.

So, machines are excited to learn. Are you ready to help them? Hold on tight. Let's first look at what machine learning is!