Klaas - Machine Learning for Finance: Data algorithms for the markets and deep learning from the ground up for financial experts and economics

There are three kinds of people who would benefit the most from this book:

This book assumes you have some working knowledge in linear algebra, statistics, probability theory, and calculus. However, you do not have to be an expert in any of those topics.

To follow the code examples, you should be comfortable with Python and the most common data science libraries, such as pandas, NumPy, and Matplotlib. The book's example code is presented in Jupyter Notebooks.

Explicit knowledge of finance is not required.

, Neural Networks and Gradient-Based Optimization , will explore what kinds of ML there are, and the motivations for using them in different areas of the financial industry. We will then learn how neural networks work and build one from scratch.

, Applying Machine Learning to Structured Data , will deal with data that resides in a fixed field within, for example, a relational database. We will walk through the process of model creation: from forming a heuristic, to building a simple model on engineered features, to a fully learned solution. On the way, we will learn about how to evaluate our models with scikit-learn, how to train tree-based methods such as random forests, and how to use Keras to build a neural network for this task.

, UtilizingComputer Vision , describes how computer vision allows us to perceive and interpret the real world at scale. In this chapter, we will learn the mechanisms with which computers can learn to identify image content. We will learn about convolutional neural networks and the Keras building blocks we need to design and train state-of-the-art computer vision models.

, Understanding Time Series , looks at the large number of tools devoted to the analysis of temporally related data. In this chapter, we will first discuss the "greatest hits" that industry professionals have been using to model time series and how to use them efficiently with Python. We will then discover how modern ML algorithms can find patterns in time series and how they are complemented by classic methods.

, Parsing Textual Data with Natural Language Processing , uses the spaCy library and a large corpus of news to discuss how common tasks such as named entity recognition and sentiment analysis can be performed quickly and efficiently. We will then learn how we can use Keras to build our own custom language models. The chapter introduces the Keras functional API, which allows us to build much more complex models that can, for instance, translate between languages.

, Using Generative Models