HandsOn Machine Learning with ScikitLearn and TensorFlo

本书《HandsOn Machine Learning》深入浅出地介绍了机器学习概念和核心算法,如线性回归、KNN。通过实例演示如何使用Scikit-Learn库进行数据集创建、模型训练与评估,帮助读者理解并掌握机器学习实践技巧。

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作者:禅与计算机程序设计艺术

1.简介

“Hands-On Machine Learning”一书作者Geron教授()和他的团队在近年推出了新版机器学习教材,该书全面、系统地阐述了机器学习的各个领域。在作者看来,现有的机器学习教材不仅难以给初学者提供足够的实践经验,而且还存在严重的偏差。为了解决这个问题,该书试图通过教授者对机器学习的实际应用问题的理解,将机器学习知识和技能从浅层次到深层次地呈现出来。作者认为,真正掌握机器学习并非易事,需要结合实际应用场景和方法论,才能真正解决复杂的问题。本文将以该书中最著名的Scikit-learn库及TensorFlow框架为例,带读者领略机器学习在实际工程中的各种应用场景和解决方案。

2.基本概念术语说明

本章节将会介绍一些机器学习相关的术语和概念,包括数据集、特征、模型、训练样本、测试样本等。阅读完本节内容后,读者可以快速了解机器学习的基础概念。

2.1 数据集 Data Set

数据集(Data set),又称为样本或样本集(Sample set)、训练集(Training set)或者是测试集(Test set)。顾名思义,数据集就是用来训练或测试模型的数据。它是由若干个元素组成的集合,每个元素通常代表一个实例(Instance),每个实例拥有相同数量的属性(Attribute)或特征(Feature)。数据的每一个元素都对应着一个标签(Label)。

2.2 特征 Feature

特征(Feature)通常指的是数据集的一个维度。特征能够帮助模型更好

When most people hear “Machine Learning,” they picture a robot: a dependable butler or a deadly Terminator depending on who you ask. But Machine Learning is not just a futuristic fantasy, it’s already here. In fact, it has been around for decades in some specialized applications, such as Optical Character Recognition (OCR). But the first ML application that really became mainstream, improving the lives of hundreds of millions of people, took over the world back in the 1990s: it was the spam filter. Not exactly a self-aware Skynet, but it does technically qualify as Machine Learning (it has actually learned so well that you seldom need to flag an email as spam anymore). It was followed by hundreds of ML applications that now quietly power hundreds of products and features that you use regularly, from better recommendations to voice search. Where does Machine Learning start and where does it end? What exactly does it mean for a machine to learn something? If I download a copy of Wikipedia, has my computer really “learned” something? Is it suddenly smarter? In this chapter we will start by clarifying what Machine Learning is and why you may want to use it. Then, before we set out to explore the Machine Learning continent, we will take a look at the map and learn about the main regions and the most notable landmarks: supervised versus unsupervised learning, online versus batch learning, instance-based versus model-based learning. Then we will look at the workflow of a typical ML project, discuss the main challenges you may face, and cover how to evaluate and fine-tune a Machine Learning system. This chapter introduces a lot of fundamental concepts (and jargon) that every data scientist should know by heart. It will be a high-level overview (the only chapter without much code), all rather simple, but you should make sure everything is crystal-clear to you before continuing to the rest of the book. So grab a coffee and let’s get started!
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron English | 2017 | ISBN: 1491962291 | 566 Pages | EPUB | 8.41 MB Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details
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