python for data science and machine learning bootcamp

are two issues to note:1, if the data is linearly non-divided.When the data is linearly non-divided, we can also use the above method, but will come to an unacceptable solution, at this time we can detect whether the solution is valid to determine whether our data can be divided.2. What happens if W0 exists in Z?In our previous assumptions, W0 represents a const

("Training"); Trainingdata.foreach (function(item) {classifier.adddocument (Item.text, Item.label); }); varStartTime =NewDate (); Classifier.train (); varEndTime =NewDate (); varTrainingtime = (endtime-starttime)/1000.0; Console.log ("Training Time:", Trainingtime, "seconds"); Loadtestdata ();}functionLoadtestdata () {Console.log ("Loading test Data"); Fs.readfile (' Test_data.json ', ' utf-8 ',function(err,

sessions should be conducted before they can be completed?In general, the number of sessions = total size of the sample/out-of-sample data. SizeHow many data should you choose to use as an out-of-sample data?The different requirements have different options, but one rule of thumb is:Out-of-sample data size = Total siz

Netfei is a DVD leasing company. by increasing its sales by 10%, it can earn 1 million RMB in revenue, which is very impressive. How to: predict consumers' ratings for movies? (Increase the predicted value by 10 percentage points through their own systems) if the recommendations you provide to consumers are very accurate, the consumers will be very satisfied. The essence of machine learning: 1. An existin

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 '

training sample Ax = titanic[["Pclass"," Age","Sex"]] aty = titanic["survived"] - #The average complement of the acquired age space -x[" Age"].fillna (x[" Age"].mean (), inplace=True) - - #split training data and test data -X_train, X_test, y_train, y_test =train_test_split (x, in y, -test_size=0.25, toRandom_state=33) + #extracting dictionary features for vectorization -VEC =Dictvectorizer () theX_train

= Polynomialfeatures (degree=4)#4-time polynomial feature generator -X_train_poly4 =poly4.fit_transform (X_train) Wu #Building Model Predictions -Regressor_poly4 =linearregression () About Regressor_poly4.fit (X_train_poly4, Y_train) $X_test_poly4 =poly4.transform (x_test) - Print("four-time linear model prediction score:", Regressor_poly4.score (X_test_poly4, Y_test))#0.8095880795746723 - - #learning and predicting using L1 norm regularization line

Hinton, one of the deep learning giants, has a classic paper visualizing data using T-sne in the field of dimensionality reduction. This method is the classic of the manifold (non-linear) data dimensionality reduction, and there are few new dimensionality reduction methods to be completely surpassed. Compared with PCA and other linear methods, this method can eff

length of 20. Now the machine has 8 GB of memory. How can this problem be solved. Iii. System Design Questions Forward maximum matching algorithm (FMM) for Chinese Word Segmentation in natural language processing ). Note: The example explains the basic idea of FMM. (1) design the data structure struct dictnote of the dictionary. (2) Use C/C ++ to implement FMM. The optional interface is Int FMM (vector He

determine the type of input vector x of the calculation process to specify the naïve Bayesian computation processBy the conditional probability formula get P (y=ck| x=x) = P (y=ck,x=x)/P (x=x) = P (x=x | Y=CK) P (y=ck)/P (x=x)The full probability formula is available (replace P (x=x)):　　　　　　　　　　　　　　　　　　　　　　　　　　　Note: Argmax refers to CK with the largest probability of taking　　　One of the I (..) is the indicator function, of course, these probabilities in the actual can be very block, you can se

)) +geom_point ()#加平滑模式Ggplot (Heights.weights, aes (x = Height, y = Weight)) +geom_point () +geom_smooth ()Ggplot (HEIGHTS.WEIGHTS[1:20,], AES (x = Height, y = Weight)) +geom_point () +geom_smooth ()Ggplot (heights.weights[1:200,], AES (x = Height, y = Weight)) +geom_point () +geom_smooth ()Ggplot (heights.weights[1:2000,], AES (x = Height, y = Weight)) +geom_point () +geom_smooth ()Ggplot (Heights.weights, aes (x = Height, y = Weight)) +Geom_point (AES (color = Gender, alpha = 0.25)) +Scale_al

ImageNet: non-commercial visualisation of big dataAs of May 1, 2015, the Imagenet database has more than 15 million images. cifar10:10 Types of object recognition data setsData set contains 60,000 images of 32*32, total 10 objects (6,000 images/class)Among them, 50,000 as training images,10,000 as testing imagesmnist : handwritten font recognition data set10 types of d