前言

Machine learning is, without a doubt, one of the hottest topics in the world right now. Most companies are using it, or planning to use it, for many applications. Some people dub machine learning as the new electricity, or the new industrial revolution. I would go a bit farther and call it the new renaissance. Why? Because in the Renaissance, progress was made in the arts, the sciences, engineering, mathematics, and almost all the fields by the same people. With machine learning, this is finally possible again. With a strong knowledge of machine learning, one is able to derive cutting edge results in almost any field one decides to apply them, and this is fascinating. And that is what this book is for, to get you up to speed with the fast-moving world of machine learning!

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毫无疑问，机器学习是当今世界上最热门的话题之一。大多数公司都在使用它，或者计划将它用于许多应用程序。有些人把机器学习称为“新电力”或“新工业革命”。我想更进一步，称它为“新文艺复兴”。为什么?因为在文艺复兴时期，同一群人在艺术、科学、工程、数学以及几乎所有领域都取得了进步。有了机器学习，这终于再次成为可能。有了强大的机器学习知识，一个人就能在几乎任何一个他决定要应用的领域中获得前沿成果，这是令人着迷的。这就是这本书的目的，让你跟上快速发展的机器学习世界!

But what is machine learning? I define it as “common sense, but for a computer.” What does this mean? It means that machine learning is the practice of getting computers to make decisions using the decision-making process that we, humans, utilize in our daily life. Humans make many decisions based on past experiences, and we can teach this decision- making process to the computer, with the difference that computers call their past experiences “data.

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但是什么是机器学习呢?我把它定义为“常识，但对电脑来说。”“这是什么意思?”这意味着机器学习是一种让计算机使用我们人类在日常生活中使用的决策过程来做决策的实践。人类根据过去的经验做出许多决定，我们可以把这个决策过程传授给计算机，不同之处在于计算机把他们过去的经验称为“数据”。

”Most approaches to machine learning require a heavy amount of mathematics, in particular, linear algebra, calculus, and probability. While a solid understanding of these topics is very useful for learning machine learning, I strongly believe that they are not absolutely necessary. What is needed to understand machine learning is a visual mind, an intuition of basic probability, and a strong desire to learn.

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机器学习的大多数方法都需要大量的数学知识，特别是线性代数、微积分和概率论。虽然对这些主题的深入理解对于学习机器学习非常有用，但我坚信它们并不是绝对必要的。理解机器学习所需要的是视觉思维，对基本概率的直觉，以及强烈的学习欲望。

In this book, I present machine learning as a series of exercises of increasing difficulty, in which the final goal is to teach a computer how to take a particular decision. Each chapter is dedicated to a different machine learning algorithm, and is focused in one use-case of this algorithm, such as spam detection, language analysis, image recognition, and so on. For the readers who are interested in programming, I also code the algorithms in Python, and teach some useful packages that are used in industry and research. The code is also shared in Git for easy download.

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在这本书中，我把机器学习作为一系列难度越来越大的练习，其最终目标是教会计算机如何做出特定的决定。每一章都专门介绍一种不同的机器学习算法，并重点介绍该算法的一个用例，如垃圾邮件检测、语言分析、图像识别等。对于那些对编程感兴趣的读者，我还用Python编写了算法代码，并教授了一些用于工业和研究的有用包。代码也在Git中中共享，便于下载。