In this chapter, we uncovered the inner workings of machine learning and deep learning. We learned the main concepts of mathematical optimization and statistics. We connected them to machine learning and, finally, learned how machines learn and how we can use optimization algorithms to define learning. Lastly, we covered popular machine learning and deep learning algorithms, including linear regression, tree ensembles, CNNs, word embeddings, and recurrent neural networks. This chapter concludes our introduction to data science.
In the next chapter, we will learn how to build and sustain a data science team capable of delivering complex cross-functional projects.