Deep Learning for Natural Language Processing by Jason Brownlee 在 Python 中为自然语言开发深度学习模型

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Data Preparation for Machine Learning Data Cleaning, Feature Selection, and Data Transforms in Python by Jason Brownlee 30 step-by-step lessons, 398 pages. intuitions, feature selection, scaling, more. 数据准备涉及将原始数据转换为可以使用机器学习算法进行建模的形式。 切开方程、希腊字母和混淆，并发现您需要了解的专门数据准备技术，以便充分利用下一个项目的数据。 使用清晰的解释、标准的 Python 库和分步教程课程，您将发现如何自信而有效地为计算机学习预测建模准备数据。

Master Machine Learning Algorithms Finally Pull Back The Curtain And See How They Work With Clear Descriptions, Step-By-Step Tutorials and Working Examples in Spreadsheets by Jason Brownlee 10 top algorithms described. 12 step-by-step tutorials, 163 pages. 16 spreadsheets with working algorithms. 你必须了解算法才能在机器学习中取得好成绩（并被公认为优秀）。 在这个巨型电子书是写在友好的机器学习掌握风格，你习惯了，最后切入数学，并了解机器学习算法是如何工作的，然后从零开始，一步一步地实现它们。

Probability is the bedrock of machine learning. You cannot develop a deep understanding and application of machine learning without it. Cut through the equations, Greek letters, and confusion, and discover the topics in probability that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of probability to machine learning, Bayesian probability, entropy, density estimation, maximum likelihood, and much more.