udacity python machine learning

), 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

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

different features to the same interval: normalization and normalizationNormalization:From sklearn.preprocessing import MinmaxscalerStandardization:From sklearn.preprocessing import StandardscalerSelect a feature that is meaningfulIf a model behaves much better than a test data set on a training dataset, it means that the model is too fit for training data.The commonly used schemes to reduce generalization errors are:(1) Collect more training data(2) Introduction of penalty by regularization(3)

享平台来找到numpy, scipy and Matplotlib, Here are all. WHL files, which need to be installed via PIP, so there is an important preparation is easy_install pip to complete the PIP installation, after the installation is successful, it can be installed on the above three respectively. WHL for installation in Pip install **.py.5. Download the most important machine learning package: Scikit-learn, the package install

In the previous chapters, we have been using the accuracy rate (accuracy) to evaluate the performance of the model, which is usually a good choice. In addition, there are many evaluation indicators, such as precision (precision), recall rate (recall) and F1 value (F1-score).Confusion matrixBefore explaining the different evaluation indicators, let's start by learning a concept: The confusion matrix (confusion matrix), which shows the matrix of the

are slightly different, and many very small elements are left in the matrix, which results from the computer processing error. Enter the following command to get the error value:>>> MyEye = Randmat*invrandmat>>> Myeye-eye (4)Matrix ([[ 0.00000000e+00, -4.44089210e-16, -4.44089210e-16, -3.33066907E-16], [ -8.88178420e-16, 2.22044605e-16, 0.00000000e+00, 5.55111512E-17], [ 4.44089210e-16, 0.00000000e+00, 0.00000000e+00, -5.55111512E-17],

]) $self.errors_=[] - - for_inchRange (self.n_iter): theerrors=0 - forXi,targetinchzip (x, y):Wuyi #calculates the error between the forecast and the actual value multiplied by the learning rate theupdate=self.eta* (target-Self.predict (xi)) -self.w_[1:]+=update*XI WuSelf.w_[0]+=update*1 -Errors + = Int (update!=0) About self.errors_.append (Errors) $ return Self - - #define the p

print ' Best Feature index:\t ', bestfeatureindex print ' Best thresh old:\t\t ', Bestthreshold ' return{' Dim ': Bestfeatureindex,' Thresh ': Bestthreshold,' accuracy ': Bestaccuracy} def Apply_model(Features,labels,model):Prediction = (features[:,model[' Dim ']] > model[' Thresh '])returnPrediction#-----------Cross validation-------------Error =0.0 forEiinchRange (len (irisfeatures)):# Select All and the one at position ' ei ':Training = Np.ones (len (irisfeatures), bool) Training[ei] =Fal

System: OS X 10.11.6 The MAC system has its own Python2.7, using the Easy_install command with its own system to install the modules online. If you need to use the PYTHON3 environment, python3.5 is invoked at the terminal input Python3 after installing the Python3.5.1, view Python version Python 2, install NumPyNumPy is a Python package. It represents "Numer

): # Extend the Input feature vector as a feature matrix linenum = featurematrix.shape[0] featurematrixin = Np.tile ( Featurevectorin, (linenum,1)) # Calculate the Euclidean distance between the matrix Diffmatrix = featurematrixin -Featurematrix Sqdiffmatrix = Diffmatrix * * 2 Distancevaluearray = Sqdiffmatrix.sum (Axis=1) Distancevaluearray = Distancevaluearray * * 0.5 return DistancevaluearrayUsed in the numpy of the more distinctive things. The practice is to first

classes in the data. - -Many, many more ... the the a total of 150 data samples the evenly distributed over 3 subspecies the 4 petals per sample, calyx shape Description - " " the the " " the 2 dividing the training set and the test set94 " " theX_train, X_test, y_train, y_test =train_test_split (Iris.data, the Iris.target, thetest_size=0.25,98Random_state=33) About - " "101 3 K Nearest Neighbor Classifier learning model and prediction102 " "10

[:, 1].max () + 1 xx1, xx2 = Np.meshgrid (Np.arange (X1_min, X1_max, resolution), Np.aran GE (X2_min, X2_max, resolution)) Z = Classifier.predict (Np.array ([Xx1.ravel (), Xx2.ravel ()]). T) Z = Z.reshape (xx1.shape) Plt.contourf (xx1, xx2, Z, alpha=0.4, Cmap=cmap) Plt.xlim (Xx1.min (), Xx1.max ()) Plt.ylim (Xx2.min (), Xx2.max ()) # Plot all samples for IDX, C1 in enumerate (Np.unique (y)): Print Idx,c1 Plt.scatter (X=x[y = = C1, 0], Y=x[y = = C1, 1], alpha=0.8, C=cmap (IDX), MARKER=M

', Standardscaler ()), ('CLF', Logisticregression (penalty='L2', random_state=0)]) train_sizes, train_scores, Test_scores= Learning_curve (ESTIMATOR=PIPE_LR, X=x_train, Y=y_train, Train_sizes=np.linspace (0.1, 1.0, ten), cv=10, N_jobs=1) Train_mean= Np.mean (Train_scores, Axis=1) TRAIN_STD= NP.STD (Train_scores, Axis=1) Test_mean= Np.mean (Test_scores, Axis=1) TEST_STD= NP.STD (Test_scores, Axis=1) Plt.plot (train_sizes, Train_mean, color='Blue', marker='0', Markersize=5, label='Training Accurac

understand computer knowledge, psychology and philosophy. Artificial intelligence consists of a very wide range of sciences, consisting of a variety of fields, such as machine learning, computer vision, and so on, in general, one of the main goals of AI research is to make machines capable of doing complex work that normally requires human intelligence. But different times, different people's understanding

(Ss_y.inverse_transform (y_test), Ss_y.inverse_transform (lr_y_predict)) $ Print("the mean square error of the linear is:", Lr_mse) -Lr_mae =Mean_absolute_error (Ss_y.inverse_transform (y_test), Ss_y.inverse_transform (lr_y_predict)) - Print("the average absolute error of the linear is:", Lr_mae) - A #evaluation of the SGD model +Sgdr_score =Sgdr.score (x_test, y_test) the Print("the default evaluation value for SGD is:", Sgdr_score) -sgdr_r_squared =R2_score (y_test, sgdr_y_predict) $ Print("

regression tree is:", Dtr.score (X_test, y_test)) - Print("the r_squared values for the flat regression tree are:", R2_score (Y_test, dtr_y_predict)) - Print("the mean square error of the regression tree is:", Mean_squared_error (Ss_y.inverse_transform (y_test), - Ss_y.inverse_transform (dtr_y_predict))) A Print("the average absolute error of the regression tree is:", Mean_absolute_error (Ss_y.inverse_transform (y_test), + Ss_y.inverse_transform (dtr_y_predict))) the - " " $ the default evalua

.score (X_train_poly2, Y_train))#0.9816421639597427Two-time linear regression model fitted curves:The fitting degree is better than 1 linear fitting.The following 4 linear regression models are performed:1 #four-time linear regression model fitting2Poly4 = Polynomialfeatures (degree=4)#4-time polynomial feature generator3X_train_poly4 =poly4.fit_transform (X_train)4 #Building Model Predictions5Regressor_poly4 =linearregression ()6 Regressor_poly4.fit (X_train_poly4, Y_train)7 #draw a graph of 2

AboutDTC =Decisiontreeclassifier () $ #Training - Dtc.fit (X_train, Y_train) - #Predicting saved results -Y_predict =dtc.predict (x_test) A + " " the 4 Model Evaluation - " " $ Print("accuracy:", Dtc.score (X_test, y_test)) the Print("Other indicators: \ n", Classification_report (Y_predict, Y_test, target_names=['died','survived'])) the " " the accuracy: 0.7811550151975684 the Other indicators: - Precision recall F1-score support in the died 0.91 0.78 0.84 236 the survived 0.58 0.80 0.67 Abo