Python 机器学习
Python machine learning
Sebastian Raschka · 初版 2015 年
5人推荐
11位读者提及
11次提及
为什么值得读
- 覆盖预处理、特征工程到模型选择全流程
- 上手极快，数章内即可完成实战并参与竞赛
- 紧密结合Python主流科学计算与学习库
机器学习与预测分析正在重塑企业及其他组织的运营方式。能够理解复杂数据中的趋势与模式对成功至关重要，已成为在极具挑战的当代市场中释放增长潜力的关键战略之一。Python 能够助您挖掘数据中的核心见解——其作为编程语言的独特功能，可助您构建复杂的算法与统计模型，揭示全新视角并解答关乎成败的关键问题。Python Machine Learning 将为您打开通往预测分析领域的大门……
Hacker News 上怎么说
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……现在这些很酷的东西。它依然是一本好书，但我更推荐一条专注于机器学习的学习路线：-《终极算法》（The Master Algorithm）：面向大众读者，能帮你了解整体概貌 - Sebastian Raschka 写的《Python机器学习》（Python Machine Learning）：教你使用 python、scikit-learn、numpy、jupyter notebooks、pandas 等工具的实操技能。4 个章节带你从零基础到能打 Kaggle，后续内容更加深入。而且理论部分讲得也够……
…cool stuff these days. It's still a great book, but I'd instead recommend a path that focuses on machine learning: - The Master Algorithm: made for a general audience, gives you a lay of the land - Python Machine Learning by Sebastian Raschka: gives you practical skills using python, scikit-learn, numpy, jupyter notebooks, pandas etc. From zero to kaggle in 4 chapters, goes deeper after that. Also goes into enough theory…
……(https://smile.amazon.com/Introduction-Probability-Chapman-St...) * 斯坦福 EE263：线性动态系统导论 (http://ee263.stanford.edu/) 初级：* 吴恩达的课程 (http://cs229.stanford.edu) * 《Python机器学习》(https://smile.amazon.com/Python-Machine-Learning-Sebastian-R...) * 《统计学习导论》(https://smile.amazon.com/Introduction-Statistical-Learning-A...) 中级：* 模式……
…(https://smile.amazon.com/Introduction-Probability-Chapman-St...) * Stanford EE263: Introduction to Linear Dynamical Systems (http://ee263.stanford.edu/) Beginner: * Andrew Ng's class (http://cs229.stanford.edu) * Python Machine Learning (https://smile.amazon.com/Python-Machine-Learning-Sebastian-R...) * An Introduction to Statistical Learning (https://smile.amazon.com/Introduction-Statistical-Learning-A...) Intermediate: * Pattern…
……背景，所以看 ESL（统计学习精要）觉得挺有意思。但我更偏向于动手实操，等到真正要在项目里干活、为自己的方案选型找依据时，我发现 ISL（统计学习导论）更适合我。# 《Python机器学习》（Python Machine Learning）超级棒的实战书！Sebastian Raschka 讲得非常好，带着你一步步走完 ML 项目的全部流程：数据预处理、特征工程、模型选择……每一步都讲得明明白白，而且……
…background so ESL was interesting to go through. But I am more of a practical person, and I found ISL to be more suited for me when it came down to working on my project and supporting my choices. # Python Machine Learning Really great hands-on book ! Sebastian Raschka manages well to guide you through all steps of a ML project data: pre-processing, feature engineering, model selection... - all the steps are defined and…
你会推荐它吗？如果想找一本侧重实战、内容全面、能通俗看懂深度学习最新进展的书，你会推荐哪本？其他一些备选推荐包括：Sebastian Raschka 和 Vahid Mirjalili 写的《Python机器学习》（Python Machine Learning），Ian Goodfellow、Yoshua Bengio 和 Aaron Courville 写的《深度学习》（花书），以及 Aurélien Géron 写的《Scikit-Learn、Keras与TensorFlow机器学习实战》
Would you recommend it? Which book would you recommend, a book with practical approach, comprehensive and readable for latest developments in dl? Some other recommendations include "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
说来也怪，他们家的书质量参差不齐，而且还有好多内容高度重叠的书。在 Packt 出版的书里，我挺喜欢 Sebastian Raschka 写的《Python机器学习》系列。除此之外，他们偶尔确实会有几篇不错的实操代码“菜谱”（recipes），但几乎没几本书是值得从头到尾精读一遍的。
It's weird how mixed quality their books are, and how many overlapping books they have. From Packt I like Sebastian Raschka's books on Python Machine Learning. Other than that from time to time they do have some good "recipes", but hardly any of their books seems worth reading cover to cover.
Sebastian 也是《Python机器学习》（Python Machine Learning）的作者：https://www.amazon.com/dp/1789955750/
Sebastian is also the author of “Python Machine Learning” https://www.amazon.com/dp/1789955750/