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.
发表于2024-12-22
Machine Learning in Action 2024 pdf epub mobi 电子书
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...
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。
评分理论推导太弱,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...
评分特别适合新手,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:“...
图书标签: 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.
对ML主要工具简单介绍 上手快 挺好 FP Tree没看 SVM/CART/AdaBoost/Apriori还需要再看看
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.
评分理论条理清楚、举重若轻。可惜程序代码水平稍差。
评分基本没有算法优化,所以还是给3星。
评分何必这么多具体的代码……
Machine Learning in Action 2024 pdf epub mobi 电子书