Machine Learning in Production is a crash course in data science and machine learning for people who need to solve real-world problems in production environments. Written for technically competent "accidental data scientists" with more curiosity and ambition than formal training, this complete and rigorous introduction stresses practice, not theory. Building on agile principles, Andrew and Adam Kelleher show how to quickly deliver significant value in production, resisting overhyped tools and unnecessary complexity.
The Pragmatic Guide to Driving Value and Disrupting Markets with Blockchain
Blockchain enables enterprises to reinvent processes and business models and to pursue radically disruptive applications. Blockchain for Business is a concise, accessible, and pragmatic guide to both the technology and the opportunities it creates. Authored by three experts from IBM's Enterprise Blockchain practice, it introduces industry-specific and cross-industry use cases, and reviews best-practice approaches to planning and delivering blockchain projects. With a relentless focus on real-world business outcomes, the authors reveal what blockchain can do, what it can't do yet, and where it's headed.
Machine Learning with scikit-learn LiveLessons
Machine Learning with scikit-learn LiveLessons is your guide to the scikit-learn library, which provides a wide range of algorithms in machine learning that are unified under a common and intuitive Python API. This enables you to explore the problem space quickly and often to arrive at an optimal—or at least satisficing—approach to your problem domain or datasets. Instructor David Mertz demonstrates the main concepts and techniques used in modern machine learning through numerous examples written in scikit-learn.