Welcome Welcome to XGBoost With Python. This book is your guide to fast gradient boosting in Python. You will discover the XGBoost Python library for gradient boosting and how to use it to develop and evaluate gradient boosting models. In this book you will discover the techniques, recipes and skills with XGBoost that you can then bring to your own machine learning projects. Gradient Boosting does have a some fascinating math under the covers, but you do not need to know it to be able to pick it up as a tool and wield it on important projects to deliver real value. From the applied perspective, gradient boosting is quite a shallow field and a motivated developer can quickly pick it up and start making very real and impactful contributions. This is my goal for you and this book is your ticket to that outcome.

Preface Machine learning algorithms dominate applied machine learning. Because algorithms are such a big part of machine learning you must spend time to get familiar with them and really understand how they work. I wrote this book to help you start this journey. You can describe machine learning algorithms using statistics, probability and linear algebra. The mathematical descriptions are very precise and often unambiguous. But this is not the only way to describe machine learning algorithms. Writing this book, I set out to describe machine learning algorithms for developers (like myself). As developers, we think in repeatable procedures. The best way to describe a machine learning algorithm for us is: 1. In terms of the representation used by the algorithm (the actual numbers stored in a file). 2. In terms of the abstract repeatable procedures used by the algorithm to learn a model from data and later to make predictions with the model. 3. With clear worked examples showing exactly how real numbers plug into the equations and what numbers to expect as output. This book cuts through the mathematical talk around machine learning algorithms and shows you exactly how they work so that you can implement them yourself in a spreadsheet, in code with your favorite programming language or however you like. Once you possess this intimate knowledge, it will always be with you. You can implement the algorithms again and again. More importantly, you can translate the behavior of an algorithm back to the underlying procedure and really know what is going on and how to get the most from it. This book is your tour of machine learning algorithms and I’m excited and honored to be your tour guide. Let’s dive in.

5000个手写数字组成的训练集，是由20*20灰度图按列展开得到的，用于训练神经网络进行数字识别

Authors: Rajkumar Venkatesan, Paul Farris, Ronald T. Wilcox

master_machine_learning_algorithms

编译好的shark4.0，内含x64，vc140的lib和include文档。包括release和debug，亲测可用。

包包含李宏毅老师的机器学习所有课件，都是在李宏毅老师个人主页下载整理的，建议配合B站李宏毅老师的Machine Learning 视频一块使用。

随机梯度下降算法SDG的MATLAB实现，数据集可到UCI数据库里下载

machine_learning(Jason Brownlee) 3. Master Machine Learning Algorithms Discover How They Work and Implement Them From Scratch,6. Machine Learning Mastery With Python Understand Your Data, Create Accurate Models and work Projects End-to-End

这个是我认为最好的机器学习的入门资料了，我还把书里面每个仿真都跑了，因为在git上下载的程序有些问题。我都改好了。