Pro Machine Learning Algorithms 免积分下载

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# Machine Learning #Apress #人工智能

本书旨在填补高级算法理解和模型调整之间的空白，提供从Excel到Python/R的实际操作指南，涵盖所有主要机器学习和深度学习算法，包括监督和无监督学习，特征工程，以及案例研究。

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图书说明：

弥合对算法如何工作的高级理解与了解螺母和螺栓以更好地调整模型之间的差距。在开发所有主要机器学习模型时，本书将为您提供信心和技能。在Pro机器学习算法中，您将首先在Excel中开发算法，以便在使用Python / R实现模型之前，实际了解可在模型中调整的所有杠杆。

您将涵盖所有主要算法：监督和无监督学习，包括线性/逻辑回归; k均值聚类; PCA; 推荐系统; 决策树; 随机森林; GBM; 和神经网络。您还将通过CNN，RNN和word2vec接触最新的深度学习文本挖掘。您不仅要学习算法，还要学习特征工程的概念，以最大限度地提高模型的性能。您将看到该理论以及案例研究，例如情绪分类，欺诈检测，推荐系统和图像识别，以便您在工业中使用的绝大多数机器学习算法中充分利用理论和实践。随着学习算法，

您应该对统计/软件编程知之甚少，在本书的最后，您应该能够自信地开展机器学习项目。

你会学到什么

  • 深入了解所有主要的机器学习和深度学习算法
  • 完全理解在构建模型时要避免的陷阱
  • 在云中实现机器学习算法
  • 通过每个算法的案例研究，采用实践方法
  • 获得集成学习的技巧，以建立更准确的模型
  • 了解R / Python编程的基础知识和深度学习的Keras框架

本书适用于谁

希望转变为数据科学角色的业务分析师/ IT专业人员。希望巩固机器学习知识的数据科学家。

下载地址：Pro Machine Learning Algorithms

更多免积分电子书，请访问：IE布克斯网

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Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R by V Kishore Ayyadevara Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers. You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence. What You Will Learn Get an in-depth understanding of all the major machine learning and deep learning algorithms Fully appreciate the pitfalls to avoid while building models Implement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithm Gain the tricks of ensemble learning to build more accurate models Discover the basics of programming in R/Python and the Keras framework for deep learning Who This Book Is For Business analysts/ IT professionals who want to transition into data science roles. Data scientists who want to solidify their knowledge in machine learning.
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