Machine Learning Theory and Applications

1Concepts, Libraries, and Essential Tools in Machine Learning and Deep Learning

Photo by Annamária Borsos

In this first chapter, we will explore the different concepts in statistical learning as well as popular open‐source libraries and tools. This chapter will serve as an introduction to the field of machine learning for those with a basic mathematical background and software development skills. In general, machine learning is used to understand the structure of the data we have at our disposal and fit that data into models to be used for anything from automating tasks to providing intelligent insights to predicting a behavior. As we will see, machine learning differs from traditional computational approaches, as we will train our algorithms on our data as opposed to explicitly coded instructions and we will use the output to automate decision‐making processes based on the data we have provided.

Machine learning, deep learning, and neural networks are branches of artificial intelligence (AI) and computer science. Specifically, deep learning is a subfield of machine learning, and neural networks are a subfield of deep learning. Deep learning is more focused on feature extraction to enable the use of large datasets. Unlike machine learning, deep learning does not require human intervention to process data.

Exploration and iterations are necessary for machine learning, and ...