Machine Learning in Action pdf epub mobi txt 电子书 下载 2025
Peter Harrington holds Bachelors and Masters Degrees in Electrical Engineering. He worked for Intel Corporation for seven years in California and China. Peter holds five US patents and his work has been published in three academic journals. He is currently the chief scientist for Zillabyte Inc. Peter spends his free time competing in programming competitions, and building 3D printers.
- 机器学习
- MachineLearning
- 数据挖掘
- python
- 人工智能
- Python
- 计算机科学
- 算法
It's been said that data is the new "dirt"—the raw material from which and on which you build the structures of the modern world. And like dirt, data can seem like a limitless, undifferentiated mass. The ability to take raw data, access it, filter it, process it, visualize it, understand it, and communicate it to others is possibly the most essential business problem for the coming decades.
"Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. By implementing the core algorithms of statistical data processing, data analysis, and data visualization as reusable computer code, you can scale your capacity for data analysis well beyond the capabilities of individual knowledge workers.
Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, you'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.
As you work through the numerous examples, you'll explore key topics like classification, numeric prediction, and clustering. Along the way, you'll be introduced to important established algorithms, such as Apriori, through which you identify association patterns in large datasets and Adaboost, a meta-algorithm that can increase the efficiency of many machine learning tasks.
具体描述
读后感
机器学习是人工智能研究领域中一个极其重要的研究方向，在现今的大数据时代背景下，捕获数据并从中萃取有价值的信息或模式，成为各行业求生存、谋发展的决定性手段，这使得这一过去为分析师和数学家所专属的研究领域越来越为人们所瞩目。 本书第一部分主要介绍机器学习基础，以...
评分客观说，完全不能当入门书。 缺少必要的证明过程，有些甚至连公式都没有。 我觉得既然要学习机器学习，光改改代码完全是不够的，起码还得知道各个算法的基本公式和过程，不幸的是，这本书没有。 就比如逻辑斯蒂回归那章，他连损失函数都没提，就开始说梯度法了。问题是梯度法的...
评分为什么我会力荐这本书？ 也许书中分类器都非常的简单，数学理论都非常的粗浅（为了看明白书中SVM分类器的训练过程，不得不去复习了二次凸优化解法，自己推导被作者略去的中间过程），算法测试也只在轻量级的数据集上完成。 不过，大可不必像其他评论一样对贬低本书。聪明的读...
评分尽管评论里对这本书褒贬不一，我觉得这些都是根据每个人不同的能力背景出发而给的评论。而对于我这样能力的人来说，这本书可以说是最适合了。我是什么能力状况呢，计算机专业背景，有那么几年开发经验，但是机器学习方面是小白。 看这本书需要一定的编程经验，但不需要很强，...
评分用户评价
何必这么多具体的代码……
评分入门好书
评分随便翻翻，当复习Python和相关库了。适合初学者。
评分书中介绍了“十大机器学习算法”中的八种，虽然不深入但是讲解清楚容易理解和上手，是本佳作。从覆盖面上来看没涉及到随机森林算法和神经网络是一个小遗憾。
评分书中介绍了“十大机器学习算法”中的八种，虽然不深入但是讲解清楚容易理解和上手，是本佳作。从覆盖面上来看没涉及到随机森林算法和神经网络是一个小遗憾。
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