Python Machine Learning By Example
by: Yuxi (Hayden) Liu
ISBN-10: 1783553111
ISBN-13: 9781783553112
Publication Date 出版日期: 2017-06-06
Print Length 页数: 344
Book Description
By finelybook
Key Features
Learn the fundamentals of machine learning and build your own intelligent applications
Master the art of building your own machine learning systems with this example-based practical guide
Work with important classification and regression algorithms and other machine learning techniques
Book Description
By finelybook
Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning.
This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead,you will learn all the important concepts such as,exploratory data analysis,data preprocessing,feature extraction,data visualization and clustering,classification,regression and model performance evaluation. With the help of various projects included,you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also,you will be guided step by step to build your own models from scratch. Toward the end,you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques.
Through this book,you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language,Python. Interesting and easy-to-follow examples,to name some,news topic classification,spam email detection,online ad click-through prediction,stock prices forecast,will keep you glued till you reach your goal.
What you will learn
Exploit the power of Python to handle data extraction,manipulation,and exploration techniques
Use Python to visualize data spread across multiple dimensions and extract useful features
Dive deep into the world of analytics to predict situations correctly
Implement machine learning classification and regression algorithms from scratch in Python
Be amazed to see the algorithms in action
Evaluate the performance of a machine learning model and optimize it
Solve interesting real-world problems using machine learning and Python as the journey unfolds
About the Author
Yuxi (Hayden) Liu is currently a data scientist working on messaging app optimization at a multinational online media corporation in Toronto,Canada. He is focusing on social graph mining,social personalization,user demographics and interests prediction,spam detection,and recommendation systems. He has worked for a few years as a data scientist at several programmatic advertising companies,where he applied his machine learning expertise in ad optimization,click-through rate and conversion rate prediction,and click fraud detection. Yuxi earned his degree from the University of Toronto,and published five IEEE transactions and conference papers during his master’s research. He finds it enjoyable to crawl data from websites and derive valuable insights. He is also an investment enthusiast.
Contents
Chapter 1. Getting Started With Python And Machine Learning
Chapter 2. Exploring The 20 Newsgroups Data Set
Chapter 3. Spam Email Detection With Naive Bayes
Chapter 4. News Topic Classification With Support Vector Machine
Chapter 5. Click-Through Prediction With Tree-Based Algorithms
Chapter 6. Click-Through Rate Prediction With Logistic Regression
Chapter 7. Stock Prices Prediction With Regression Algorithms
Chapter 8. Best Practices
图书说明
主要特征
了解机器学习的基础知识并构建自己的智能应用程序
掌握建立自己的机器学习系统的艺术，以此基于实例的实践指南
使用重要的分类和回归算法等机器学习技术
图书说明
数据科学和机器学习是当今技术世界的热门话题。机器学习的重要兴趣在于使数据挖掘和贝叶斯分析比以往任何时候都更受欢迎的同样因素。这本书是您机器学习的入门点。
本书首先介绍机器学习和Python语言，并向您展示如何完成设置。展望未来，您将学习所有重要的概念，如探索性数据分析，数据预处理，特征提取，数据可视化和聚类，分类，回归和模型绩效评估。在各种项目的帮助下，您会发现有趣的是获取几种重要的机器学习算法的机制 – 他们并没有像他们想象的那样模糊。此外，您将一步一脚从头开始构建自己的模型。最后，您将收集机器学习生态系统和应用机器学习技术的最佳实践的广泛信息。
通过这本书，您将学习解决数据驱动的问题，并以强大而简单的语言Python实现您的解决方案。有趣和易于理解的例子，列出一些新闻主题分类，垃圾邮件检测，在线广告点击率预测，股价预测，将保持你的粘贴，直到达到你的目标。
你会学到什么
利用Python的强大功能来处理数据提取，操纵和探索技术
使用Python可视化跨多个维度的数据，并提取有用的功能
深入分析世界，正确预测情况
在Python中实现机器学习分类和回归算法
惊讶于看到算法在行动
评估机器学习模型的性能并对其进行优化
随着旅程的发展，使用机器学习和Python解决有趣的现实世界问题
关于作者
玉溪（海登）刘先生目前是一家数据科学家，致力于在加拿大多伦多的一家跨国在线媒体公司进行消息传递应用优化工作。他专注于社交图形挖掘，社会个性化，用户人口统计和兴趣预测，垃圾邮件检测和推荐系统。他曾在几个计划广告公司担任数据科学家，在广告优化，点击率和转化率预测中应用机器学习专长，并点击欺诈检测。玉溪在多伦多大学获得学士学位，并在硕士研究生期间发表了五项IEEE交易和会议论文。他发现从网站上抓取数据并获得有价值的见解。他也是投资爱好者。
目录
第1章Python和机器学习入门
第二章探索20个新闻组数据集
第3章使用朴素贝叶斯的垃圾邮件检测
第4章支持向量机的新闻主题分类
第5章基于树的算法的点击式预测
第六章逻辑回归的点击率预测
第七章股价预测与回归算法
第八章最佳实践
