I. Preamble
Microsoft's machine learning framework in 2018 moonrise version 0.1, released in May 2019 version 1.0. During the differences between the versions (including namespaces, methods, etc.) is quite large, with the version 1.0 release, it should become stable. Before the garden also see a lot of articles related to the introduction of my study provided a lot of help. Due to the current data is not very rich, so the learning process is also quite a few detours, this series of articles mainly recorded some of my experiences in the learning process, and some of the details will be explained in detail, hoping to learn the basis of zero for the machine students provide some help. (C # can not do zero-based)
Second, the basic concept
1. What is Machine Learning?
Definition: a computer program to complete the task (T), if the computer experience acquired on T (E) on the performance of the more (P), the better, then we can say that this program 'learning' experience on T's.
Simple explanation of what is called "machine learning": If you enter, the more experience the better the performance, which is called "learning." For example: the traditional program logic is based on the algorithm, the same algorithm in the case of the program is to run for 100 years will not have the ability to upgrade, but the machine learning is based on the data (sample), and in the case of the same algorithm under the more valid the data accumulated, the stronger program performance capability.
2, learning to solve the problem of the difference between the traditional algorithm and problem solving through the machine
Problems to be solved: meeting rooms come in on a boy and asked him to stand in front of the camera, evaluate the program through a body if the boy well.
(1) Traditional Solutions
First, we analyze to determine how well a person body, the main judgments features include: height, weight, measurements, etc., and then through a measure algorithm (such as BMI) is calculated, the process is as follows:
(2) machine learning algorithms
Machine learning is dependent on sample data, so the idea is to solve this problem like this:
① First, you have to take to the streets to collect data, asking height, weight, measurements data you interviewed, and based on your experience gave him a judge, to form the following table:
② The data collected for training the model is formed, and then judge objects to be determined by the model. Process is as follows:
小结：通过这个问题的解决，感觉通过机器学习来解决问题比传统方法麻烦多了，是的，对于身材判断这样的问题，人类可以很简单找到一个逻辑分析的方法，所以通过逻辑算法解决就比较方便，但有时候很多事情我们人类是很容易处理的，但我们却不知道其中的逻辑，比如：判断一张图片是否是18+图片，判断一片论文是否写得很好，判断一个人是否长得漂亮等等。这些问题人类很轻松就能处理，但无法总结出其中的规律并交给机器去执行，这时候机器学习算法就可以派上用场了。
三、机器学习的流程
机器学习的流程如下：
数据准备 -> 建模 -> 训练 -> 评估 -> 应用
在实际应用时，由于训练的过程可能时间比较长，所以我们会分两个阶段进行：
1、学习阶段：数据准备 -> 建模 -> 训练 -> 评估 -> 保存模型
2、消费阶段：读取模型 -> 应用
评估的过程就是对模型的检验，我们一般会把样本数据随机分成两份，其中一部分用来学习，另一部分用来检验模型效果，判断一下我们的模型能力。
以上是涉及到机器学习的有些最基础的理论知识，下面几篇文章会由浅入深介绍ML.NET的一些应用。
系列文章目录：
机器学习框架ML.NET学习笔记【1】基本概念
机器学习框架ML.NET学习笔记【2】入门之二元分类
机器学习框架ML.NET学习笔记【3】文本特征分析
机器学习框架ML.NET学习笔记【4】手写数字识别
机器学习框架ML.NET学习笔记【5】手写数字识别（续）
机器学习框架ML.NET学习笔记【6】TensorFlow图片分类
机器学习框架ML.NET学习笔记【7】人物图片颜值判断
机器学习框架ML.NET学习笔记【8】目标检测
机器学习框架ML.NET学习笔记【9】自动学习
资源下载：
项目源码：https://github.com/seabluescn/Study_ML.NET
资源文件：https://gitee.com/seabluescn/ML_Assets
（由于资源文件较大，所以放在码云平台提供下载）