"Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, …
To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and …
Updated with the latest Maven coordinates, Java programming features, and API changes, this book is your guide to solving problems in writing asynchronous and event-based programs Key Features Explore a variety of tools and techniques used to solve problems in implementing concu…