Machine learning is an integral part of many commercial applications and research projects today, in areas ranging from medical diagnosis and treatment to finding your friends on social networks. Many people think that machine learning can only be applied by large companies with extensive research teams. In this book, we want to show you how easy it can be to build machine learning solutions yourself, and how to best go about it. With the knowledge in this book, you can build your own system for finding out how people feel on Twitter, or making predictions about global warming. The applications of machine learning are endless and, with the amount of data available today, mostly limited by your imagination

最新版非常清晰的彩色的pdf + 源代码，作者通过具体的例子，应用两款非常流行的Python框架：Scikit-Learn和TensorFlow，帮助你掌握构建机器学习系统所需要的概念和工具。

hands on machine learning with scikit learn and tensorflow PDF及随书代码

详细介绍了机器学习的方法，主要是通过python实现的，

Mastering Machine Learning With Scikit-learn的第二版，英文

Hands-On Machine Learning with Scikit-Learn and TensorFlow 英文原版

scikit-learn 0.19 中文文档pdf完整文字版，共507页， 文档打开口令：beijing8106

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 2ed 2019.pdf Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow SECOND EDITION Concepts, Tools, and Techniques to Build Intelligent Systems

SciKit learn的简称是SKlearn，是一个python库，专门用于机器学习的模块。 SKlearn包含的机器学习方式： 分类，回归，无监督，数据降维，数据预处理等等，包含了常见的大部分机器学习方法。

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. * Explore the machine learning landscape, particularly neural nets * Use Scikit-Learn to track an example machine-learning project end-to-end * Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods * Use the TensorFlow library to build and train neural nets * Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning * Learn techniques for training and scaling deep neural nets