If you want to get into machine learning but fear the math, this book is your ultimate guide. Specifically designed for non-mathematicians, this useful guide presents a breakdown of each variant of machine learning, with examples and working code. You'll learn the various algorithms, data preparation techniques, trees, and networks, and get acquainted with the tools that help you get more from your data. You'll understand how it works, where it's used, and how to make it great. Learn the languages of machine learning: Weka, Mahout™, Spark™, and R Make the right data storage and cleaning decisions, tailored to your desired output Understand decision trees, Bayesian networks, artificial neural networks, and association rule learning Implement support vector machines knowing the relevant advantages and limitations Apply Big Data processing techniques with Hadoop®, Mahout, and MapReduce Use Spring XD to capture streaming data and learn in real time Access the tools you need to plan your project and acquire and process data Study examples and use provided working code for hands-on learning