Data Science & AI Books

If one is reading this, they are likely interested in solving science and engineering problems. They are also likely taking a course that requires them to learn MATLAB. They might ask themselves why they should learn to code, reasoning that if they had wanted to major in computers, they would have. The most obvious reason is that there are almost n

R is one of the most popular, powerful data analytics languages and environments in use by data scientists. Actionable business data is often stored in Relational Database Management Systems (RDBMS), and one of the most widely used RDBMS is Microsoft SQL Server. Much more than a database server, it's a rich ecostructure with advanced analytic capab

This open access book offers a concise and gentle introduction to finite element programming in Python based on the popular FEniCS software library. Using a series of examples, including the Poisson equation, the equations of linear elasticity, the incompressible Navier - Stokes equations, and systems of nonlinear advection - diffusion - reaction e

This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. The

In today's rapidly changing AI technology environment, software engineers often struggle to build real-world applications with large language models (LLM). The benefits of incorporating open source LLMs into existing workflows is often offset by the need to create custom components. That's where Haystack comes in. This open source framework is a co

This open access book describes the technologies needed to construct a secure big data infrastructure that connects data owners, analytical institutions, and user institutions in a circle of trust. It begins by discussing the most relevant technical issues involved in creating safe and privacy-preserving big data distribution platforms, and especia

This open access book bridges the gap between playing with robots in school and studying robotics at the upper undergraduate and graduate levels to prepare for careers in industry and research. Robotic algorithms are presented formally, but using only mathematics known by high-school and first-year college students, such as calculus, matrices and p

With the big data boom, the rise of the Internet of Things (IoT), and the development of artificial intelligence (AI) applications, we've entered a new era of smart data. Unfortunately, not many enterprises are ready for it. Some companies are deficient in data management, while others lack standard data engineering systems. Some simply lag behind

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis a

If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer and