Machine Learning For Business Analytics Concepts Techniques And Applications With Jmp Pro 2nd Edition 2nd Galit Shmueli by Galit Shmueli, Peter C. Bruce, Mia L. Stephens, Muralidhara Anandamurthy, Nitin R. Patel 9781119903833, 1119903831 instant download after payment.

An up-to-date introduction to a market-leading platform for data analysis and machine learning Machine learning is the process by which computer algorithms can be made progressively more effective through the accumulation of data and experience. A subset of artificial intelligence, it is an increasingly powerful business tool, driving ever more refined data analysis and decision-making. In recent years, JMP Pro® has emerged as one of the most effective and widely-used commercial analysis and data mining tools on the market, using machine learning to produce automated solutions to a range of data analysis needs. Machine Learning for Business Analytics: Concepts, Techniques and Applications with JMP Pro®, 2nd ed. offers an accessible and engaging introduction to machine learning using this market-leading software. It provides concrete examples and case studies to educate new users and deepen existing users’ understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses. Machine Learning for Business Analytics: Concepts, Techniques and Applications with JMP Pro®, 2nd ed. readers will also find: Updated material which improves the book’s usefulness as a reference for professionals beyond the classroom Four new chapters, covering topics including Text Mining and Responsible Data Science A guide to JMP Pro®’s new features and enhanced functionality Machine Learning for Business Analytics: Concepts, Techniques and Applications with JMP Pro®, 2nd ed. is ideal for students and instructors of business analytics and data mining classes, as well as data science practitioners and professionals in data-driven industries.