机器学习实战
机器学习实战 PDF 分享
机器学习是人工智能领域中一个非常重要的研究方向。在当今大数据时代的背景下，捕获数据并从中提取有价值的信息或模式已成为各个行业生存和发展的决定性手段。这使得这个过去只属于分析师和数学家的研究领域越来越受到重视。
本书第一部分主要介绍机器学习的基础以及如何使用算法进行分类，并逐步介绍各种经典的监督学习算法，如k-最近邻算法、朴素贝叶斯算法、逻辑回归算法、支持向量机、AdaBoost积分法、，基于树的回归算法和分类回归树（CART）算法。第三部分重点介绍了无监督学习及其主要算法：K-means聚类算法、Apriori算法、FP-growth算法。第四部分介绍了机器学习算法的一些辅助工具。
通过精心安排的示例，本书切入日常工作任务，摒弃学术语言，并使用高效的可重用Python代码来解释如何处理统计数据、进行数据分析和可视化。通过各种示例，读者可以学习机器学习的核心算法，并将其应用于一些战略性任务，如分类、预测和推荐。此外，它们还可以用于实现更高级的功能，如摘要简化。
Machine learning is an extremely important research direction in the field of artificial intelligence. In the context of today’s big data era, capturing data and extracting valuable information or patterns from it has become a decisive means for various industries to survive and develop. This makes this research field, which was exclusive to analysts and mathematicians in the past, more and more attention.
The first part of this book mainly introduces the basis of machine learning and how to use algorithms for classification, and gradually introduces a variety of classical supervised learning algorithms, such as k-nearest neighbor algorithm, naive Bayes algorithm, logistic regression algorithm, support vector machine, AdaBoost integration method, tree based regression algorithm and classified regression tree (CART) algorithm. The third part focuses on unsupervised learning and some of its main algorithms: K-means clustering algorithm, Apriori algorithm, FP growth algorithm. The fourth part introduces some auxiliary tools of machine learning algorithm.
Through carefully arranged examples, the book cuts into daily work tasks, abandons academic language, and uses efficient reusable Python code to explain how to process statistical data, conduct data analysis and visualization. Through various examples, readers can learn the core algorithm of machine learning and apply it to some strategic tasks, such as classification, prediction and recommendation. In addition, they can also be used to implement some more advanced functions, such as summary simplification.
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