Machine learning is an exciting new way to use computers to perform tasks that require the ability to learn from experience. In order to make machine learning a reality, programmers rely on special languages, such as Python and R, and new types of tools. Machine Learning For Dummies helps the reader understand what machine learning is, when it can help perform a new class of computer tasks, and how to implement machine learning using Python and R, along with the required tools. Unlike most machine learning books, Machine Learning For Dummies does not assume that the reader has years of experience using programming languages. This book provides the much-needed entry point for people who really could use machine learning to accomplish practical tasks, but dont necessarily have the skills required to use on more advanced books. This book will cover the entry level materials required to get readers up and running faster, how to perform practical tasks, how to perform useful work without getting overly involved in the underlying math principles, fun ways to play with new tools and learn as a result, and how to separate facts from myth to see how machine learning is useful in todays world. Topics Covered: Getting the Real Story about AI Learning in the Age of Big Data Having a Glance at the Future Coding in R Using R Studio Coding in Python Using Anaconda Exploring Other Machine Learning Tools Demystifying the Math Behind Machine Learning Descending the Right Curve Validating Machine Learning Starting with Simple Learners Preprocessing Data Starting Easy with Linear Models Going a Step Beyond using Support Vector Machines Resorting to Ensembles of Learners.
- Table of Contents(第v頁)
- Part 1 Introducing How Machines Learn(第7頁)
- Part 2 Preparing Your Learning Tools(第45頁)
- Part 3 Getting Started with the Math Basics(第145頁)
- Part 4 Learning from Smart and Big Data(第217頁)
- Part 5 Applying Learning to Real Problems(第331頁)
- Part 6 The Part of Tens(第383頁)