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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.

This book is based upon the book Data Structures and Algorithms in Java by Goodrich and Tamassia, and the related Data Structures and Algorithms in C++ by Goodrich, Tamassia, and Mount. However, this book is not simply a translation of those other books to Python. In adapting the material for this book, we have significantly redesigned the organization and content of the book as follows: • The code base has been entirely redesigned to take advantage of the features of Python, such as use of generators for iterating elements of a collection. • Many algorithms that were presented as pseudo-code in the Java and C++ versions are directly presented as complete Python code. • In general, ADTs are defined to have consistent interface with Python’s built- in data types and those in Python’s collections module. • Chapter 5 provides an in-depth exploration of the dynamic array-based un- derpinnings of Python’s built-in list, tuple, and str classes. New Appendix A serves as an additional reference regarding the functionality of the str class. • Over 450 illustrations have been created or revised. • New and revised exercises bring the overall total number to 750.

主要责任者 Szepesvári, Csaba. 题名 Algorithms for reinforcement learning [electronic resource] / Csaba Szepesvári. 出版资料 San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2010. 摘要附注 Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.

python版 算法基础第五版 Foundation Of Algorithms 各章节代码，以及课后习题代码 各章节（1-11章）的代码基本上都有。习题只有写代码的有参考答案。证明没有，个人做的。如有错误欢迎纠正。本书图灵出版社翻译的错误不少，建议对照原英文版观看。

Two Dimensional Phase Unwrapping Theory Algorithms and Software，扫描文档，清晰度一般。

非线性模型预测控制：理论和算法 图书 Nonlinear Model Predictive Control: Theory and Algorithms，Grüne Lars, Pannek Jürgen

里面含有中文版、英文原版和答案。 非常经典的一本计算机算法书，学习算法的必看书本。 自第一版出版以来，已经成为世界范围内广泛使用的大学教材和专业人员的标准参考手册。本书全面论述了算法的内容，从一定深度上涵盖了算法的诸多方面，同时其讲授和分析方法又兼顾了各个层次读者的接受能力。