Python Machine Learning Blueprints[July 2016]下载

Machine learning is rapidly becoming a fixture in our data-driven world. It is relied upon in fields as diverse as robotics and medicine to retail and publishing. In this book, you will learn how to build real-world machine learning applications step by step. Working through easy-to-understand projects, you will learn how to process various types of data and how and when to apply different machine learning techniques such as supervised or unsupervised learning. Each of the projects in this book provides educational as well as practical value. For example, you'll learn how to use clustering techniques to find bargain airfares and how to use linear regression to find a cheap apartment. This book will teach you how to use machine learning to collect, analyze, and act on massive quantities of data in an approachable, no-nonsense manner. What this book covers Chapter 1, The Python Machine Learning Ecosystem, delves into Python, which has a deep and active developer community, and many of these developers come from the scientific community as well. This has provided Python with a rich array of libraries for scientific computing. In this chapter, we will discuss the features of these key libraries and how to prepare your environment to best utilize them. Chapter 2, Build an App to Find Underpriced Apartments, guides us to build our first machine learning application, and we begin with a minimal but practical example: building an application to identify underpriced apartments. By the end of this chapter, we will create an application that will make finding the right apartment a bit easier. Chapter 3, Build an App to Find Cheap Airfares, demonstrates how to build an application that continually monitors fare pricing. Once an anomalous price appears, our app will generate an alert that we can quickly act on. Chapter 4, Forecast the IPO Market using Logistic Regression, shows how we can use machine learning to decide which IPOs are worth a closer look and which ones we may want to skip. Chapter 5, Create a Custom Newsfeed, covers how to build a system that understands your taste in news and will send you a personally tailored newsletter each day. Chapter 6, Predict whether Your Content Will Go Viral, examines some of the most shared content and attempts to find the common elements that differentiate it from the content that people are less willing to share. Chapter 7, Forecast the Stock Market with Machine Learning, discusses how to build and test a trading strategy. There are countless pitfalls to avoid when trying to devise your own system, and it is quite nearly an impossible task. However, it can be a lot of fun, and sometimes, it can even be profitable. Chapter 8, Build an Image Similarity Engine, helps you construct an advanced, image-based, deep learning application. We will also cover deep learning algorithms to understand why they are so important and why there is such a hype surrounding them. Chapter 9, Build a Chatbot, demonstrates how to construct a chatbot from scratch. Along the way, you'll learn more about the history of the field and its future prospects. Chapter 10, Build a Recommendation Engine, explores the different varieties of recommendation systems. We'll see how they're implemented commercially and how they work. We will also implement our own recommendation engine to find GitHub repos.