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
这本书的最大好处是让你能够用最基本的pyton语法，从底层上让你构建代码，实现我们常说的比如邮件过滤，数据分类的应用。很多时候你要写最基本的代码和结构去做这些工作，而不是像kaggle的tutorial或者其他的工程大多数告诉你一个lib库函数去调用，你能看到底层在干什么...
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～
評分理论推导太弱，导致部分代码实现难以理解为什么是这样写，建议配合吴恩达讲义使用。 另外贝叶斯那段代码实现应该是错误的，作者在计算概率的时候把分母给弄错了，还有就是因为python版本问题，在python3上跑书上程序需要对程序进行一些改动。 附代码修改： def classifyNB(vec2...
評分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。
評分机器学习是概率统计的高级应用，数学知识很重要，要先掌握的先修课程有，微积分，线性代数，概率统计，多元微积分，微分方程，离散数学，数值分析，最优化，数学建模，掌握机器学习和深度学习算法，还有熟悉一种编程语言，有了这些基础，才能得心应手，机器学习主要应用在数据...
用戶評價
一般般
评分隨便翻翻，當復習Python和相關庫瞭。適閤初學者。
评分何必這麼多具體的代碼……
评分基本沒有算法優化，所以還是給3星。
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.
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