# Machine Learning some search record

Reading time: 5 minutes and 26 seconds with 1196 words. 本文总阅读量

• （1）每个格都是从1到9中的一个整数；
• （2）九宫格中的数字不会重复；
• （3）每行的和都相等；
• （4）每列的和都相等；
• （5）两条对角线上的和也相等

MATLAB中进行基于SVM的数据分析

Analyticsvidhya,一个相当不错的计算机学习网站。

A Complete Tutorial on Tree Based Modeling from Scratch (in R & Python)十分清楚,需要一定时间理解

R语言和Python语言比较经常被人们用来做机器学习，其实包括lisp,prolog,java等都是可以的。 完整的R语言教程

ipython机器学习.Ipython是一个比较有用的python IDE界面，提供比较友好的输出调试界面(网页上操作),所以顺便也查了一下怎么用。

1. 下载安装 pyzmq, 在这里不建议使用pip, pip对pyzmq支持不太好，装不上。我尝试使用easy_install c:>easy_install.exe pyzmq
2. 下载安装 jinja2, c:>easy_install.exe jinja2
好了，使用下面命令就可以把Notebook起来： c:>ipython.exe notebook

dot reference

digraph G1{

compound=true;
graph[fontname=Kaiti];
edge[fontname=SimSun];
node[fontname=SimSun];
MachineLearning[shape=box,style=rounded,fillcolor="#1f8842"];
subgraph cluster_1{

//    SL[shape=ellipse,label="Supervised Learning"];
label="Supervised Learning(This algorithm consist of a target / outcome
\nvariable (or dependent variable) which is to be predicted
\nfrom a given set of predictors (independent variables))";
node[style=filled,fillcolor=chartreuse];

Reg[label="1. Linear Regression"];
LR[label="2. logistic Regression"];
DT[label="3. Decision Tree"];
SVM[label="4. Supported Vector Machine"];
RF[label="5. Random Forest"];
KNN[label="6. KNN"];
NB[label="7. Naive Bayes"];

Reg->LR->DT->SVM->RF->KNN->NB->Reg;
}
subgraph cluster_2{

node[style=filled,fillcolor=chartreuse];
//USL[shape=ellipse,label="UnSupervised Learning"];
label="UnSupervised Learning(do not have any target or outcome variable to
\npredict / estimate.  It is used for clustering population
\nin different groups, which is widely used for
\nsegmenting customers in different groups for specific intervention )";
AA[label="Apriori Alogirithm"];
KM[label="K-means"];
}
subgraph cluster_3{

node[style=filled,fillcolor=chartreuse];
label="Reinforcement Learning(It works this way: the machine is
\nexposed to an environment where it
\ntrains itself continually using trial and error.
\nThis machine learns from past experience
\nand tries to capture the best possible knowledge
\nto make accurate business decisions )";
MDP[label="Markov Decision Process"]
}

//MachineLearning->{SL USL RL};