Machine learning(ML) techniques are being applied in a variety of fields, and data scientists are being sought after in many different industries. With machine learning, we identify the processes through which we gain knowledge that is not readily apparent from data in order to make decisions. Applications of machine learning techniques may vary greatly, and are found in disciplines as diverse as medicine, finance, and advertising.
In this chapter, we'll present different machine learning approaches, techniques, some of their applications to real-world problems, and we'll also introduce one of the major open source packages available in Python for machine learning, PyTorch. This will lay the foundation for the later chapters in which we'll focus on a particular type of machine learning approach using neural networks, which will aim to emulate brain functionality. In particular, we will focus on deep learning. Deep learning makes use of more advanced neural networks than those used during the 1980s. This is not only a result of recent developments in the theory, but also advancements in computer hardware. This chapter will summarize what machine learning is and what it can do, preparing the reader to better understand how deep learning differentiates itself from popular traditional machine learning techniques.
This chapter will cover the following topics:
- Introduction to machine learning
- Different machine learning approaches
- Neural networks
- Introduction to PyTorch
