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
特别适合新手，特别适合新手，特别适合新手。长度适中，举例形象，概念浅显通俗。难得有一个条理清楚 逻辑不迷糊 不堆砌代码打哈哈的书。基于这个理由bonus给五星，以后给别人推荐就这本了。 尤其是前面几章，介绍机器学习的基本概念。作者给我们指明了一个做ML的基本要求：“...
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...
評分 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...
用戶評價
對ML主要工具簡單介紹 上手快 挺好 FP Tree沒看 SVM/CART/AdaBoost/Apriori還需要再看看
评分讀它是為瞭熟悉Python語言；內容是在不敢恭維。
评分哇FP growth簡直美
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.
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