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...
评分人工智能的脉络 机器学习是人工智能的一个分支。 人工智能的研究历史有着一条从以“推理”为重点，到以“知识”为重点，再到以“学习”为重点的自然、清晰的脉络。 机器学习是实现人工智能的一个途径，即以机器学习为手段解决人工智能中的问题。 从学习方式来讲，机器学习包括...
评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...
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1. 这本书的价值是提供了一系列有趣的「实验作业」和「对应的数据」，以及乱七八糟的 Python 代码，迫使读者在同样数据集上自己写一个更好的。 2. 作者的 Python 代码写得真的真的很渣。 3. 作者的 SVM 写错了，不是 Platt 的原始 SMO 算法，里面的 error cache 形同虚设。 ...
用户评价
哇FP growth简直美
评分读了LR，ada boost，略读了svm，psvm。数学渣子的福音，码农最爱的实例。 虽然大家都说写的不好，不过入个门还是不错。
评分哇FP growth简直美
评分没学习又想学机器学习的可以考虑从这本书入手。偏向于应用的一本不错的入门书
评分内容比较基础，有py代码，对着看比较容易理解。
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