Mastering machine learning is a desirable skill nowadays given its vast application everywhere, from business to academia. Nevertheless, just understanding the theory of it will only take you so far since practitioners also need to understand their tools to be self-sufficient and capable.
In this chapter, we started with a high-level introduction to machine learning and learned when to use each of the machine learning types; from classification and regression to clustering and reinforcement learning. We then learned about scikit-learn and why practitioners recommend it when solving both supervised and unsupervised learning problems. To keep this book self-sufficient, we also covered the basics of data manipulation for those who haven't used libraries such as pandas and Matplotlib before. In the following chapters, we will continue to combine our understanding of the underlying theory of machine learning with more practical examples using scikit-learn.
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