Python Machine Learning

Machine learning is a hot topic today, due in no small part to the rapid increase in computing power that allows a host of tasks to be performed on a desktop machine. However, machine learning can be overwhelming to the inexperienced. That's why Python Machine Learning provides a step-by-step approach that first educates you on the underlying technologies, enabling you to build on that knowledge base as you develop your machine learning skills. You'll first explore the libraries used in Python to facilitate machine learning, including NumPy, Pandas, and matplotlib. Once you have a firm foundation in those, you'll look at machine learning using Python and the Scikit-Learn libraries, where you'll discover how various algorithms such as regression, clustering, and classification work behind the scenes. There's a chapter showing how to perform machine learning using the Microsoft Azure Machine Learning Studio, which lets you start building models without needing to code. Finally, you'll learn how to deploy the models you have built to be used by client applications. Written for those without a strong background in machine learning, Python Machine Learning covers: Fundamental libraries used in Python to enable machine learning: NumPy, Pandas, and matplotlib, Common machine learning algorithms including regression, classification, clustering, and anomaly detection, Machine learning using Python and the Scikit-Learn libraries, Deploying machine learning models as web services, Machine learning using Microsoft Azure Machine Learning Studio, Real-world examples showing how machine learning creates solutions Book jacket.