Machine Learning in Action pdf epub mobi txt 電子書 下載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.
- 機器學習
- MachineLearning
- 數據挖掘
- python
- 人工智能
- Python
- 計算機科學
- 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.
具體描述
讀後感
如果你是机器学习的入门者，如果你想快速看到算法的执行效果，那么这本书适合你。 作者把算法的基本原理讲的很清楚，而且代码是完整可执行的。当然，如果你想了解算法背后的数学原理，还需要花时间去复习一下概率论、高等数学和线性代数。 BTW：读者最好有编程经验，有抽象思维。
評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，...
評分这本书最大的优点在于有源码实现，很赞，但是理论部分太差了，看了逻辑回归和支持向量机两章，发现好多理论都没讲，就比如逻辑回归中的Cost函数都没说，如果不了解，源码读起来也是一头雾水，所以对于初学者还需要一本理论较强的书，推荐李航博士的统计机器学习方法，刚好配套～
評分 評分我的学习过程如下，供大家参考： 1、有些python的基础编程能力，如果没有，先花半个小时学习下； 2、数学基本统计基础，如果不懂数学原理，可以先不要去理解数学原理； 3、先上手写下代码，沉浸进入，熟悉了代码流程，再回头去看数据原理，就明白了。 5、一句话，先不求甚解，...
用戶評價
隨便翻翻，當復習Python和相關庫瞭。適閤初學者。
评分理論條理清楚、舉重若輕。可惜程序代碼水平稍差。
评分over simplified in maths, you do need refer to other textbooks for get better idea how it works. and too much coding details, I can understand as the author was from CS background, but I think you need read more, beside this is indeed a nice start point.
评分教你把Thinkers和Doers結閤起來。思想與代碼並舉
评分何必這麼多具體的代碼……
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