PyCharm实现机器学习实战入门指南

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资源摘要信息: "MachineLearninginAction"是一本由Peter Harrington所著的机器学习实战书籍,书中详细介绍了基于Python编程语言和PyCharm集成开发环境下的机器学习入门及应用。PyCharm作为一个专业的Python IDE,提供了代码编辑、调试、测试等功能,极大地方便了开发者进行机器学习项目的开发。 本资源以"Machine Learning in Action"为标题,主要围绕着机器学习算法的实现和应用进行介绍。书中以Python语言为工具,带领读者逐步了解并掌握机器学习的基础知识和核心算法。读者可以通过阅读本书来了解机器学习的基本概念,学会如何使用Python进行数据处理、模型构建和算法调优,最终能够独立完成机器学习项目的开发。 描述中提到的"01 k近邻算法"是机器学习中一种基础的分类算法,属于监督学习的范畴。k-近邻算法的核心思想是通过测量不同特征值之间的距离来进行分类。算法中的“k”代表最近邻的数量,即在特征空间中找到最近的k个邻居,然后根据这些邻居的类别信息来对新的数据点进行分类。该算法简单直观,易于理解和实现,但计算量较大,对大数据集的处理速度较慢。本书详细解释了k-近邻算法的工作原理、实现步骤,并通过PyCharm环境下的具体编程实践来加深读者的理解。 书中不仅涉及到了k-近邻算法,还可能涵盖了更多机器学习的算法和模型,如线性回归、逻辑回归、支持向量机、决策树、随机森林、梯度提升树等。每一种算法的介绍都可能以实际的代码实现作为切入点,强调理论与实践相结合,帮助读者在实践中掌握机器学习技术。 该书的代码示例可以在下载的"MachineLearninginAction-master"压缩包文件中找到。这个压缩包文件应该是包含了书中所有示例代码的完整集合,读者可以解压后按照书中指导在PyCharm中打开和运行这些代码。通过这种方式,读者可以亲手尝试每一个算法的运行过程,对机器学习的原理和应用有更加深刻的认识。 此外,书中还可能探讨了机器学习项目开发的各个阶段,包括数据预处理、特征选择、模型训练、模型评估和模型部署等,使得整个机器学习项目的开发流程变得清晰和系统化。读者在阅读和实践的过程中,不仅可以学习到具体的机器学习算法,还能掌握机器学习项目的开发流程。 整本书的结构和内容设置,都旨在帮助读者构建一个完整的机器学习知识体系。从初学者的角度出发,一步步引导读者熟悉和精通机器学习,成为能够独立解决实际问题的机器学习工程师。 标签"Python"指出了这本书主要使用Python编程语言进行讲解。Python由于其简洁易读的语法、强大的社区支持以及丰富的数据科学相关库(如NumPy、Pandas、Scikit-learn等)而成为数据科学和机器学习领域内非常流行的语言。Peter Harrington的这本书正是基于Python的这些优势,向读者展示如何用Python在PyCharm中实现机器学习算法和项目。 总结来说,本书不仅介绍了机器学习的基础知识和关键算法,还通过PyCharm这一强大工具来引导读者实际操作和实践,从而达到理论与实际相结合的教学目的。通过阅读和实践本书,读者将获得宝贵的机器学习经验,为进入数据科学领域打下坚实的基础。
2018-07-30 上传
Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. 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. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. What's InsideA no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos =================================== Table of ContentsPART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce