Data Science & AI Books

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

This third ebook in the series introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then easily deploy those models for consumption as cloud web services. The ebook presents an overview of modern data science theory and princ

Moore's law has finally run out of steam for CPUs. The number of x86 cores that can be placed cost-effectively on a single chip has reached a practical limit, making higher densities prohibitively expensive for most applications. Fortunately, for big data analytics, machine learning, and database applications, a more capable and cost-effective alte

Artificial intelligence is already changing industry landscapes, with early adopters reporting benefits in high-value business cases such as fraud detection, preventative maintenance, and recommendation engines. Yet working on an AI initiative is demanding for many enterprises, whether you're in the middle of the process or just getting started. Th

AI is transforming numerous industries. Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects. This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. After reading Machine Learning Yearning, you will be able to: - Prioritize the most promising direction

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data vi

Traditional data architecture patterns are severely limited. To use these patterns, you have to ETL data into each tool - a cost-prohibitive process for making warehouse features available to all of your data. The lack of flexibility with these patterns requires you to lock into a set of priority tools and formats, which creates data silos and data

Over the past 10 years, data analytics and data visualization have become essential components of an enterprise information strategy. And yet, the adoption of data analytics has remained remarkably static, reaching no more than 30% of potential users. This book explores the most important techniques for taking that adoption further: embedding analy