English | 2015 | ISBN: 1784399086 | 186 Pages | True PDF | 1.6 MB
This book is intended for data technologists (scientists, analysts, or developers) with previous machine learning experience who are also comfortable reading code in Python. This book is ideal for those looking for a way to deliver results quickly to enable rapid iteration and improvement.

Machine learning is the process of teaching machines to remember data patterns, using them to predict future outcomes, and offering choices that would appeal to individuals based on their past preferences.

The book begins with an introduction to test-driven machine learning and quantifying model quality. From there, you will test a neural network, predict values with regression, and build upon regression techniques with logistic regression. You will discover how to test different approaches to Naive Bayes and compare them quantitatively, along with learning how to apply OOP (Object Oriented Programming) and OOP patterns to test-driven code, leveraging scikit-Learn.

Finally, you will walk through the development of an algorithm which maximizes the expected value of profit for a marketing campaign, by combining one of the classifiers covered with the multiple regression example in the book.