学习Machine Learning书籍分级推荐

机器学习进阶指南

最新推荐文章于 2025-09-18 11:42:58 发布

转载最新推荐文章于 2025-09-18 11:42:58 发布 · 5.2k 阅读

本文提供了机器学习技术学习路径及推荐书籍，分为五个层级：新手级掌握数据科学基础；学徒级学会应用不同算法；熟手级理解算法原理；大师级深入研究高级主题；宗师级自由探索并贡献社区。

Machine Learning

《How To Get Better At Machine Learning》推荐将学习Maching Learning技术划分为几个不同阶段，并为每个阶段的学习者推荐了参考书籍，在此转载一下 :

“ His roadmap into machine learning is in turn broken down into 5 levels, each pointing to a specific textbook to master. The five levels are:

Level 0 (Neophyte): Read Data Smart: Using Data Science to Transform Information into Insight. Assumes you know your way around excel and will finish up knowing about the existence and maybe the high-level data flow of a few algorithms.
Level 1 (Apprentice): Read Machine Learning with R. Learn when to apply different machine learning algorithms, and use R to do so. Assumes maybe a little programming, algebra, calculus and probability, but only a little.
Level 2 (Journeyman): Read Pattern Recognition and Machine Learning. Discover why machine learning algorithms work from a maths perspective. Interpret and debug the output of machine learning methods and have knowledge of deeper machine learning concepts. Assumes working knowledge of algorithms, good linear algebra, some vector calculus, some algorithm implementation experience.
Level 3 (Master): Read Probabilistic Graphical Models: Principles and Techniques. Go deep into advanced topics like convex optimization, combinatorial optimization, probability theory, differential geometry, and other maths. Get good at probabilistic graphical models, when to use them and how to interpret their results.
Level 4 (Grandmaster): Take on whatever you like. Give back to the community.

It’s a nice breakdown, and Colorado provides specific chapter suggestions for each level as well as a suggested capstone project. ”

这里还有一篇不错的关于机器学习的博客《计算机的潜意识》：

机器学习方法是计算机利用已有的数据(经验)，得出了某种模型(迟到的规律)，并利用此模型预测未来(是否迟到)的一种方法。从广义上来说，机器学习是一种能够赋予机器学习的能力以此让它完成直接编程无法完成的功能的方法。但从实践的意义上来说，机器学习是一种通过利用数据，训练出模型，然后使用模型预测的一种方法。