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Machine learning and Data Mining recommendation book listWith these books, no longer worry about the class no sister paper should do. Take your time, learn, and uncover the mystery of machine learning and data mining."

Summary: What is data mining. What is machine learning. And how to do python data preprocessing. This article will lead us to understand data mining and machine learning technology, through the Taobao commodity case data preproces

First, the visualization method Bar chart Pie chart Box-line Diagram (box chart) Bubble chart Histogram Kernel density estimation (KDE) diagram Line Surface Chart Network Diagram Scatter chart Tree Chart Violin chart Square Chart Three-dimensional diagram Second, interactive tools Ipython, Ipython Notebook plotly Iii. Python IDE Type Pycharm, specifying a Java swing-based user interface PyDev, SWT-based

(written in front) said yesterday to write a machine learning book, then write one today. This book is mainly used for beginners, very basic, suitable for sophomore, junior to see the children, of course, if you are a senior or a senior senior not seen machine learning is also applicable. Whether it's studying intellig

). INT). Set (parent. Time)//holds intermediate values to make the Algo easier to read audit x: = new (big. Int) Y: = new (big. INT)//1-(Block_timestamp-parent_timestamp)//X.sub (Bigtime, Bigparenttime) x.div (x, BIG10) x.sub (big 1, x)//MAX (1-(Block_timestamp-parent_timestamp)// -99) if X.CMP (BIGMINUS99) ?Postscript?The concept of the POW algorithm is simple, that is, the working side submits hard-to-calculate but easy-to-validate calculations, and the other nodes verify the results to make

What is http://www.quora.com/What-is-data-science data science?Http://www.quora.com/How-do-I-become-a-data-scientist how can I become a data scientist?Http://www.quora.com/Data-Science/How-does-data-science-differ-from-traditional-statistical-analysis How does the scientific data differ from the traditional statistical analysis?CourseHTTP://STATISTICS.BERKELEY.EDU/CLASSES/S133/Computational data concept, Berkeleyhttp://www.cs.berkeley.edu/~jordan/courses/294-fall09/Practical

the VC dimension theory, we need more data to get the same generalization ability.For the second case, there is the same reason. We also inadvertently enlarged the size of the hypothesis set.can refer to Raymond Paul Mapa generalization theory (lesson six)There are two ways to resolve this:1, avoid data snooping. -_-2, can not avoid in the calculation of generalization theory when the data snooping into consideration. For example, consider increasing the complexity of the hypothesis set, increa

Summary:Orange Orange is a component-based data mining and machine learning software suite that features a friendly, yet powerful, fast and versatile visual programming front end for browsing data analysis and visualization, and the base binds Python for scripting development. It packs Orange Orange is a component-based data

Turn from:"Basics" Common machine learning Data mining knowledge pointsBasis (Basic):MSE (Mean square error mean squared error), LMS (leastmean square min squared), LSM (Least square Methods least squares), MLE (Maximumlikelihood Estimation maximum likelihood estimation), QP (quadratic programming two-time plan), CP (Conditional probability conditional probabili

algorithm)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).Learning to Rank (based o

)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).Learning to Rank (based on

algorithm), GA (Genetic algorithm genetic algorithm)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).

Ten classic algorithms in machine learning and Data Mining Background: In the early stage of the top 10 algorithm, Professor Wu made a report on the top 10 challenges of Data Mining in Hong Kong. After the meeting, a mainland professor put forward a similar idea. Professor Wu felt very good and began to solve the probl