原文：5 free e-books for machine learning mastery?
作者：Serdar Yegulalp?翻譯：賴信濤?責編：仲培藝

There are few subjects in computing as?fascinating, or intimidating, as machine learning. Let's face it -- you can't master machine learning in a weekend, and at the very least it requires a good grasp of the underlying?mathematical principles.

That said, if you have the math chops, you'll want to augment your use of machine learning frameworks (there are plenty to pick from) with a good understanding of the theory behind them.

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Here are five high-quality, free-to-read texts that provide introductions to and explanations of machine learning's ins and outs. Some have code examples, but most focus on formulas and theory; in principle, they can be applied to any number of languages, frameworks, or problems.

A Course in Machine Learning

The gist:?A highly readable text designed to provide an extremely beginner-friendly approach to the topic. The book is a work in progress -- some sections are still marked TODO -- but what it lacks in completeness, it makes up in sheer accessibility.

Target audience:?Anyone with a good grasp of calculus, probability, and linear algebra. No expertise in any specific language is required.

Code content:?Some pseudocode; the majority of what's presented is concepts and formulas.

The Elements of Statistical Learning

The gist:?A 500-plus-page text that covers what the authors describe as "learning from data," the processes of employing statistics that are the underpinnings for?machine learning. It's been through two editions and 10 printings since 2001, for good reason -- it covers a massive amount of territory and isn't limited to any one field.

Target audience:?Those who already have a good foundation in math and statistics and don't need a lot of hand-holding to translate their math skills into good code.

Code content:?None. This isn't a software development text; this is about foundational concepts around machine learning.

Mentioned in this article

• A Course in Machine LearningLEARN MORE on?Hal Daumé III
• The Elements of Statistical Learning, 2nd Ed.LEARN MORE on?Stanford University
• Bayesian Reasoning and Machine LearningLEARN MORE on?David Barber
• Gaussian Processes for Machine LearningLEARN MORE on?Gaussian Processes for Machine...
• Machine LearningLEARN MORE on?InTech

Bayesian Reasoning and Machine Learning

The gist:?Bayesian methods are behind everything from spam filters to pattern recognition, so they constitute a major field of study for machine-learning mavens. This text walks through all the major aspects of Bayesian statistics, and how they apply to common scenarios in machine learning.

Target audience:?Anyone with a good grasp of calculus, probability, and linear algebra.

Code content:?Lots! Each chapter contains both pseudocode and links to a toolkit of actual code demos. That said, the code is not in Python or R, but is code for the commercial MATLAB environment, although?GNU Octave?can work as an open source substitute.

Gaussian Processes for Machine Learning

The gist:?Gaussian processes are part of the family of analyses used by Bayesian methods. This text focuses on how Gaussian concepts can be used in common machine learning methods like classification, regression, and model training.

Target audience:?Roughly the same as "Bayesian Reasoning and Machine Learning."

Code content:?Most of the code featured in the book is pesudocode, but like "Bayesian Reasoning and Machine Learning," the?appendices include examples for MATLAB/Octave.

Machine Learning

The gist:?A collection of essays on different and highly specific aspects of machine learning. Some are more general and philosophical; others are focused on specific problem domains, such as "Machine Learning Methods for Spoken Dialogue?Simulation and Optimization."

Target audience:?Intended for lay readers as well as the more technically inclined.

Code content:?Virtually none, although formulas abound. Read for flavor.

計算機中有一些領域非常令人著迷，或令人畏懼，機器學習就是這樣。精通機器學習并非一朝之事，至少，你需要花一些時間掌握必備的數學知識。

也就是說，如果你數學很好，那么就會更加理解機器學習框架背后的原理，使用起來也會得心應手。

下面介紹5本高質量的、免費閱讀的電子書，主要是對機器學習的介紹和解釋。其中有一些有代碼示例，但是一般都是專注于公式和理論的，這些原理可以應用到各種語言、框架和問題。

A Course in Machine Learning

要點：為初學者準備的初涉機器學習的高質量文檔。此書仍在撰寫中——有一些章節依然標記著TODO——但是其高可讀性完全可以彌補這部分不足。

目標讀者：任何掌握微積分、概率論和線性代數的人都可以閱讀此書，不需要有任何編程語言專長。

代碼內容：有一些偽代碼，不過此書大部分用來展示的東西還是原理和公式。

The Elements of Statistical Learning

要點：超過500頁的文本，據作者稱，具體陳述了如何“從數據中學習”，對機器學習崗位需求的急劇升高顯示了這個領域的熱門程度。此書自2001年已經出版過兩個版本并印刷了10次，此書還有一大好處：跨度很大，不局限于一個領域。

目標讀者：統計學和數學基礎較好的、不需要將自己的數學形式轉換成代碼的人。

代碼內容：沒有。這并不是一本軟件開發的書，而是關于機器學習的理論基礎。

Bayesian Reasoning and Machine Learning

要點內容：?Bayesian（貝葉斯）方法是所有有關模式識別和垃圾過濾的基礎，所以逐漸形成了一個特殊的領域。此書涵蓋Bayesian統計的各個主要方面，闡述了它是如何應用的。

目標讀者：任何有微積分、概率論和線性代數基礎的人。

代碼內容：很多！每一個章節都有偽代碼和工具的鏈接，以及一些demo。而且，代碼并不是Python或R語言的，而是商業MATLAB環境，GNU Octave也可以作為一個開源的替代品。

Gaussian Processes for Machine Learning

重點內容：高斯處理也是貝葉斯方法的一部分。本書集中討論如何在一般機器學習方法中使用高斯原理，例如分類、回歸和模型訓練等。

目標讀者：大致和Bayesian Reasoning and Machine Learning差不多。

代碼內容：書中使用的代碼大多是偽代碼，但是和ayesian Reasoning and Machine Learning一樣，有些MATLAB/Octave代碼。

Machine Learning

重點內容：一個論文集，包括很多不同方面、內容深奧的機器學習知識。其中一些比較抽象，另一些專注于特定的問題，比如“模擬對話的機器學習方法”等。

目標讀者：想要在這方面深入學習的人。

代碼內容：有一些公式，沒有代碼。

總結

以上是生活随笔為你收集整理的精通机器学习的5本免费电子书(5 free e-books for machine learning mastery)的全部內容，希望文章能夠幫你解決所遇到的問題。

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