Python Machine Learning: An All-in-One Guide
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Summary
The book has 20 chapters in 5 parts. Part 1, Getting Started, covers Python machine learning basics, setting up a machine learning environment, machine learning fundamentals, and the mathematical foundations of statistical analysis; Part 2, Data Preprocessing, covers generating and loading datasets and data preprocessing; Part 3, Machine Learning Algorithms, covers regression analysis, decision tree analysis, support vector machines, cluster analysis, ensemble learning, neural network learning, convolutional network learning, and model evaluation; Part 4, Machine Learning Applications, presents four applications: image recognition, speech recognition, journal and news classification, and image compression; Part 5, Hands-On Projects, covers social friend analysis, e-commerce click-through rate prediction, and more.
Python Machine Learning in One Book is suited to students and practitioners who want to understand traditional machine learning algorithms, programmers who want to know how to implement machine learning algorithms efficiently, and staff and managers who want to understand how machine learning algorithms can be applied.
Editor's review
Who it's for
A good fit for
- Students and junior practitioners who want a systematic understanding of traditional machine learning algorithms
- Programmers with Python experience who want to implement machine learning algorithms efficiently
- Staff and business managers who need to understand how machine learning is applied in practice
- Self-learners preparing to practice image recognition, classification, or click-through rate prediction
Not a good fit for
- Pure theory readers with no programming experience who do not intend to write code
- People interested only in cutting-edge deep learning models and unwilling to learn traditional algorithms and preprocessing
- Readers who hope to complete large industrial-grade projects independently after reading one book
Reading tips
- Read in the order of the five parts, and after learning each algorithm, reproduce it with your own dataset.
- Those with weak mathematical foundations can skip derivations first, focus on running the code to get the process working, and then return to the formulas.
- After reading the applications part, choose a case from the project practice part, do it independently from beginning to end, and write a review.
About the publisher
Peking University Press is a comprehensive university press whose publishing areas cover classical literature, popular science, social culture, world history, Western philosophy, and culture. Its catalogue contains 6,031 titles, with representative books including A Dream of Red Mansions, Relativity: The Special and General Theory (15th Edition) (with Einstein's classic papers on unified field theory and other topics), Elements, Conics, The Aristocratic Families of the Eastern Jin, The Philosophy of Mythology (Volume I), The Philosophy of Mythology (Volume II), and The Origin of Continents and Oceans. Representative authors include Cao Xueqin, Zhou Ruchang, Albert Einstein, Euclid, Apollonius, Tian Yuqing, Schelling, and Xian Gang. The press has developed distinctive strengths in classical Chinese literature and in the translation of Western philosophical classics, while also covering scientific classics and social and cultural studies. As public information is limited, this description is based only on the books in the catalogue and offers no further extrapolation.
Contents
- 版权信息
- 内容提要
- 序
- 前言
- 第一篇 基础入门篇
- 第1章 Python机器学习入门
- 第2章 设置机器学习的环境
- 第3章 Python机器学习基础
- 第4章 统计分析数学基础及Python实现
- 第二篇 数据预处理篇
- 第5章 数据分析第一步——产生和加载数据集
- 第6章 数据分析第二步——数据预处理
- 第三篇 机器学习算法篇
- 第7章 回归分析
- 第8章 分类算法——决策树学习
- 第9章 支持向量机
- 第10章 聚类算法——聚类分析
- 第11章 集成学习算法——集成学习
- 第12章 神经网络学习
- 第13章 神经网络算法2——卷积神经网络
- 第14章 模型评价
- 第四篇 机器学习应用篇
- 第15章 图像识别
- 第16章 语音识别
- 第17章 中文期刊分类
- 第18章 图像压缩
- 第五篇 项目实战篇
- 第19章 社交好友分析
- 第20章 电商点击率预测及分析
- 附录 本书所用Python函数、方法、类
FAQ
Can I read this book without a machine learning background?
How broad is the algorithm coverage in the book?
What can I learn from the project practice part?
Can I learn by reading without practicing?
Comments
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About the author
Yang Zhixiao is one of the authors included in this Chinese book database, with a single title to date: Python Machine Learning in One Book, which is classified here under programming and design. As far as currently available information goes, public material about Yang Zhixiao personally is limited; the author's English name, nationality, life details, and educational background cannot be confirmed, so little can be said about personal experience. Judging from the work itself, the book is aimed at readers who wish to enter the field of machine learning using Python as a tool, combining instruction in programming fundamentals with common machine learning methods in a technical style that emphasizes getting started and hands-on practice. Books of this kind usually feature clear steps, runnable examples, and a gradual chapter structure that lets readers learn while practicing. Yang Zhixiao's writing largely follows this approach, focusing on bringing concepts down to the level of code and lowering the barrier for beginners. In terms of influence, the book offers Chinese readers a fairly direct introductory path and is suitable as a starting point for learning Python machine learning; further details about the author await additional public information.
Fan Yanfeng's publicly available information is currently limited. The author's representative work is Python Machine Learning in One Book, which is classified under programming and design. Judging from the title and its category, the book is aimed at readers who wish to enter the field of machine learning through Python, and it likely combines the basics of Python programming with commonly used machine learning methods. It reads as a technical tutorial that balances introductory guidance with practical use and emphasizes a complete learning path in a single volume. The author organizes the material around this subject in a way that leans toward hands-on operation and readability, helping beginners connect language tools with algorithm practice. Based on the available library information, there are no reliable records of the author's nationality, dates of birth and death, educational background, or professional experience, so no speculation is made about the author's life. The work reflects an approach that integrates learning a programming language with machine learning applications and offers some reference value for readers seeking a systematic introduction.
