面向软件工程师的机器学习：学习成为机器学习工程师的完整日常计划

daal4py-Intel（R）oneAPI数据分析库的便捷Python API Intel:registered:oneAPI数据分析库的简化API，可以快速使用适合数据科学家或机器学习用户的框架。 旨在帮助提供对Intel（R）oneAPI数据分析库的抽象，以直接使用或集成到自己的框架中，并通过为scikit-learn提供嵌入式缓存来扩展这一范围。 使用daal4py优化补丁运行完整的scikit-learn测试套件： 当从PyPi应用于scikit学习时 当应用于从master分支构建时 在媒体上关注我们 我们在Medium上发布博客，因此请学习在daal4py的帮助下进行更有效的数据分析的技巧。 这是我们的最新博客： 重要连结 支持 使用以下方法报告问题，提出问题并提供建议： 您可以通过与项目维护者私下 安装 可以从conda-forge安装daal4py（推荐）： conda install daal4py -c conda-forge 或通过英特尔渠道： conda install daal4py -c intel 您也可以 。 入门 daal4py的核心功

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously.

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