python machine learning bootcamp

), though it's no better than Microsoft's Visual Studio, but it's much more than the one that comes with it-if it's written in C, Helpless is written in Java, startup speed huge slow ~ ~Recently turned over the book "Machine Learning in Action". The book uses Python to implement some machine

Python Machine Learning Practical tutorialsShare Network address--https://pan.baidu.com/s/1miib4og Password: WTIWThe course is really good, share to everyoneMachine Learning (machines learning, ML) is a multidisciplinary interdisciplinary subject involving probability theory

1> supervised Learning (classification): First let the machine learn the sample data of each flower, and then let him according to this information, the non-marked flowers of the type of image classification.2> Characteristics: We call the results of all measurements in the data a feature.2> cross-validation: Extreme call-to-law (leave-one-out) takes a sample from the training set and trains a model on the

Sample of the data provided in the machine learning in action, which is said to be the characteristics of each candidate on a dating site, and how much the current person likes them. A total of 1k data, the first 900 as a training sample, the last 100 as a test sample.The data format is as follows:468933.5629760.445386didntlike81783.2304821.331698smalldoses557833.6125481.551911didntlike11480.0000000.332365s

[i]) if (classifierresu Lt! = Datinglabels[i]): ErrOrcount + = 1.0 print "The total error rate is:%f"% (Errorcount/float (numtestvecs)) Print error count def img2vector (filename): Returnvect = zeros ((1,1024)) FR = open ( FileName) For I in range (+): LINESTR = Fr.readline () F or J in range (+): RETURNVECT[0,32*I+J] = Int (linestr[j]) RETURN RET Urnvectdef handwritingclasstest (): hwlabels = [] trainingfilelist = Listdir (' trainingDigits ') #load the training

In the model training, especially in the training set to do cross-validation, usually want to save the model, and then put on a separate test set test, the following is the Python training model to save and reuse.Scikit-learn already has the model persisted operation, the import joblib canfromimport joblibModel Save>>> Os.chdir ( "Workspace/model_save" ) >>> from sklearn import SVM >>> X = [[0 , 0 ], [1 , 1 ]]>>> y = [ 0 , 1 ]>>> CLF = SVM. SV

Python code implementation on the perception machine ----- Statistical Learning Method Reference: http://shpshao.blog.51cto.com/1931202/1119113 1 #! /Usr/bin/ENV Python 2 #-*-coding: UTF-8-*-3 #4 # Untitled. PY 5 #6 # copyright 2013 T-dofan There are still a few questions, the book's adjustment strategy is: Wi = wi

Before installing Scikit-learn, you need to install numpy,scipy. However, there are always errors when installing scipy (pip install scipy). After a series of lookups, the reason is that scipy relies on numpy and many other libraries (such as Lapack/blas), but these libraries are not easily accessible under Windows.After finding, the discovery can be solved by another way, http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpyDownload here: Numpy-1.11.2+mkl-cp34-cp34m-win32.whl Scipy-0.18.1-c

Small task: Achieve picture classification1. Picture materialPython bulk compress jpg images: PiL library resizehttp://blog.csdn.net/u012234115/article/details/502484092. Environment ConstructionInstallation version of Python under Windows comparison 2.7 vs 3.6Https://pypi.python.org/pypiInstallation of the PIL Library under WindowsHttps://pypi.python.org/pypiInstallation of the PIL Library under Windowshttp://zjfsharp.iteye.com/blog/2311523Installati

is the custom of naming in Python? I found that if the variable name was completely expanded, it would be too long-my MacBook Pro was too ugly to show up. This is followed by the variable shorthand naming of C + +.V. Entrance Call functionThe main function, similar to C + +. As soon as you run the knn.py script, the code is executed first:if __name__ = = ' __main__ ': print "You are running knn.py " CLASSIFYSAMPLEFILEBYKNN (' datingSetOne.txt '

Prediction problems in machine learning are usually divided into 2 categories: regression and classification .Simply put, regression is a predictive value, and classification is a label that classifies data.This article describes how to use Python for basic data fitting, and how to analyze the error of fitting results.This example uses a 2-time function with a ra

module. But this and the original SSH ratio is still not very stable, not very useful. Not suitable for production environments. To be useful or to change the native SSH, but we will not, we will only change Python. In short this chapter is to achieve a fortress machine function, really want to do a good thing to say later.The more famous is probably this: jumpserver-open-source Springboard machineLong con

Stitching raw DATA:Train_data = pd.read_csv (' train.csv ') Test_data = pd.read_csv (' test.csv ') All_data = Np.vstack ((train_data.ix[:,1:-1], TEST_DATA.IX[:,1:-1]))Merge array Vstack and Hstack functions under NumPy:>>> a = Np.ones ((2,2)) >>> B = Np.eye (2) >>> print Np.vstack ((A, b)) [[1. 1.] [1. 1.] [1. 0.] [0. 1.]]>>> Print Np.hstack ((A, b)) [[1. 1. 1. 0.] [1. 1. 0. 1.]Generate a high (2) secondary feature:def group_data (data, degree=2, Hash=hash): new_data = [] m,n = D

(labels[:: -1]) Xgtrain = XGB. Dmatrix (Train[offset:,:], Label=labels[offset:]) Xgval = XGB. Dmatrix (Train[:offset,:], label=labels[:offset]) watchlist = [(Xgtrain, ' Train '), (Xgval, ' val ')]model = Xgb.train (plst , Xgtrain, Num_rounds, watchlist, early_stopping_rounds=120) preds2 = Model.predict (xgtest,ntree_limit=model.best_ Iteration) #combine Predictions#since the metric only cares on relative rank we don ' t need to Averagepreds = (PREDS1) * *. 4 + (PREDS2) *8.6return Preds(Code fro

data.X = [[1.,-1., 2.], [2., 0., 0.], [0.,1.,-1.]] Binarizer= preprocessing. Binarizer (). Fit (X)#The default threshold value is 0.0PrintBinarizer#Binarizer (copy=true, threshold=0.0)Printbinarizer.transform (X)#[1.0. 1.]#[1.0. 0.]#[0.1. 0.]Binarizer= preprocessing. Binarizer (threshold=1.1)#set the threshold value to 1.1Printbinarizer.transform (X)#[0.0. 1.]#[1.0. 0.]#[0.0. 0.]4. Label preprocessing (label preprocessing)4.1) Label binary value (label binarization)Labelbinarizer is typica

-0.576525 11.778922 0-0.346811-1.678730 1-2.124484 2.672471 11.217916 9.597015 0-0.733928 9.098687 0-3.642001-1.618087 10.315985 3.523953 11.416614 9.619232 0-0.386323 3.989286 10.556921 8.294984 11.224863 11.587360 0-1.347803-2.406051 11.196604 4.951851 10.275221 9.543647 00.470575 9.332488 0-1.889567 9.542662 0-1.527893 12.150579 0-1.185247 11.309318 0-0.445678 3.297303 11.042222 6.105155 1-0.618787 10.320986 01.152083 0.548467 10.828534 2.676045 1-1.237728 10.549033 0-0.683565-2.166125 10.229

Citycluster[label[i]].append (Cityname[i]) #将每个簇的城市输出For I in range (len (citycluster)):Print ("expenses:%.2f"% expenses[i]) #将每个簇的平均花费输出Print (Citycluster[i])Click to run, you can come out results.Where the N_clusters class, the consumption level of similar cities gathered in a classExpense: The numerical plus of the central point of the cluster, that is, the average consumption levelImplementation process:1, establish the project, import Sklearn related packageImport NumPy as NPFrom Sklearn.cl

)]=1 else:print "The word:%s is not in my vocabulary!" %word return returnvecdef TRAINNBC (trainsamples,traincategory): Numtrainsamp=len (Trainsamples) NumWords=len (train Samples[0]) pabusive=sum (traincategory)/float (numtrainsamp) #y =1 or 0 feature Count P0num=np.ones (numwords) P1NUM=NP.O NES (numwords) #y =1 or 0 category count P0numtotal=numwords p1numtotal=numwords for I in Range (Numtrainsamp): if Traincategory[i]==1:p0num+=trainsamples[i] P0numtotal+=sum (Trainsamples[i]) E

attribute in the data set. The general situation is somewhere between the two.D. High-dimensional mappingMap properties to high-dimensional space. This is the most precise approach, which completely retains all the information and does not add any additional information. For example, Google, Baidu's CTR Prediction model, pre-processing will be all the variables to deal with this, up to hundreds of millions of dimensions. The benefit of this is that the entire information of the original data is

=linearr.predict (X_train) #基于训练集得到的线性y值Plt.figure ()Plt.scatter (x_train,y_train,color= ' green ') #原始训练集数据散点图Plt.plot (x_train,y_train_pred,color= ' black ', linewidth=4) #线性回归的拟合线Plt.title (' Train ') #标题Plt.show ()Y_test_pred=linearr.predict (X_test)Plt.scatter (x_test,y_test,color= ' green ') #绘制测试集数据散点图Plt.plot (x_test,y_test_pred,color= ' black ', linewidth=4) #基于线性回归的预测线Plt.title (' Test ')Plt.show ()Print (' mse= ', Sm.mean_squared_error (y_test,y_test_pred)) #MSE值Print (' r2= ', Sm.r2_