coursera machine learning python

1. Install Pipmkdir ~/vi ~/.pip/pip.conf[global]trusted-host=mirrors.aliyun.comindex -url=http://https://bootstrap.pypa.io/get-pip.pysudo python get---9.0. 1 from/usr/local/lib/python2. 7 2.7)2. Install the Machine learning PackageThe following installation package is not chaotic due to dependenciessudo Install sudo install sudo install sudo install scipyError:S

============================================================================================ "Machine Learning Combat" series blog is Bo master reading " Machine learning Combat This book's notes, including the understanding of the algorithm and the Python code implementatio

as the similarity of two vectors.The commonly used kernel functions are: Polynomial cores: , which is the threshold value, is the index set by the user. Hyperbolic tangent (sigmoid) Cores: Radial basis function core (Gaussian core): Now summarize the steps of the nuclear PCA, taking the RBF nucleus as an example:1 compute the kernel (similarity) matrix K, which is the calculation of any two training samples:Get K:For example, if the training set has 10

2.7.x,python 3.3.X and Python 3.4.X four series packages, which is a legacy of other distributions. Therefore, in various operating systems, whether it is Linux, or Windows, MAC, it is recommended anaconda!Since Anacoda is a collection of Python science and technology packages, different packages follow the same protocol, and you can see http://docs.continuum.io

This article describes the python Machine Learning Decision tree in detail (demo-trees, DTs) is an unsupervised learning method for classification and regression. Advantages: low computing complexity, easy to understand output results, insensitive to missing median values, and the ability to process irrelevant feature

, or K nearest neighbor (Knn,k-nearestneighbor) classification algorithm, is one of the simplest methods in data mining classification technology. The so-called K nearest neighbor is the meaning of K's closest neighbour, saying that each sample can be represented by its nearest K-neighbor.The core idea of the KNN algorithm is that if the majority of the k nearest samples in a feature space belong to a category, the sample also falls into this category and has the characteristics of the sample on

First of all, to collect ...This article is for the author after learning Zhou Zhihua Teacher's machine study material, writes after the class exercises the programming question. Previously placed in the answer post, now re-organized, will need to implement the code to take out the part of the individual, slowly accumulate. Want to write a machine

in the first section, the meta-algorithm briefly describesIn the case of rare cases, the hospital organizes a group of experts to conduct clinical consultations to analyze the case to determine the outcome. As with the panel's clinical consultations, it is often better to summarize a large number of individual opinions than a person's decision. Machine learning also absorbed the ' Three Stooges top Zhuge Li

!accuracy:87.07%******************* SVM ********************Training took3831. 564000s!accuracy:94.35%******************* GBDT ********************In this data set, because the cluster of data distribution is better (if you understand this database, see its T-sne map can be seen.) Since the task is simple, it has been considered a toy dataset in the deep learning boundary, so KNN has a good effect. GBDT is a very good algorithm, in Kaggle and other bi

Python3 Learning using the APIA sample of a data structure of a dictionary type, extracting features and converting them into vector formSOURCE Git:https://github.com/linyi0604/machinelearningCode:1 fromSklearn.feature_extractionImportDictvectorizer2 3 " "4 dictionary feature Extractor:5 pumping and vectorization of dictionary data Structures6 category type features vectorization with 0 12 values using prototype feature names7 numeric type features r

called the polynomial model, but its class conditional probability calculation formula is not accurate.Referencesalgorithm Grocer--naive Bayesian classification of classification algorithm (Naive Bayesian classification)study of naive Bayesian text classification algorithmThe author of this paper, Adan, derives from: The classical algorithm of machine learning and the implementation of

This section learns to use Sklearn for voting classification, see a specific example, the dataset uses the Iris DataSet, using only the sepal width and petal length two dimension features, Category we also only use two categories: Iris-versicolor and Iris-virginica, the standard uses ROC AUC.Python Machine learning Chinese catalog (http://www.aibbt.com/a/20787.html)Reprint please specify the source,

Recently learned about Python implementation of common machine learning algorithms on GitHubDirectory First, linear regression 1. Cost function2. Gradient Descent algorithm3. Normalization of the mean value4. Final running result5, using the linear model in the Scikit-learn library to implement Second, logistic regression 1. Cost funct

Full Stack Engineer Development Manual (author: Shangpeng) Python Tutorial Full solution installation Pip Install LIGHTGBM Gitup Web site: Https://github.com/Microsoft/LightGBM Chinese Course http://lightgbm.apachecn.org/cn/latest/index.html LIGHTGBM Introduction The emergence of xgboost, let data migrant workers farewell to the traditional machine learning algo

()--------------------------------------------------------------------------------------------------------------- ---------------------------------------At lastCode SummaryImport NumPy as Npimport cv2from matplotlib import pyplot as PltX = Np.random.randint (25,50, (25,2)) Y = Np.random.randint (6 0,85, (25,2)) Z = Np.vstack ((x, y)) # Convert to np.float32z = Np.float32 (Z) plt.hist (z,100,[0,100]), Plt.show () # define Criteria and apply Kmeans () criteria = (CV2. Term_criteria_eps + CV2. Ter

Print "Performing greedy feature selection ..." score_hist = []n = 10good_features = Set ([]) # greedy Feature selection LOOPW Hile Len (score_hist) if f not in good_features: feats = List (good_features) + [f] Xt = Sparse.hstack ([xts[j] for J in feats]). TOCSR () C5/>score = Cv_loop (Xt, y, model, N) Scores.append ((score, F)) print "Feature:%i Mean AUC:%f"% (f, score) g Ood_features.add (sorted (scores) [ -1][1]) Score_hist.append (sorted

=true) # drop useless columns and create LABELSIDX = test.id.values.astype (int) test = Test.drop ([' id ', ' tube_assembly_id ', ' quote_date '), Axis = 1) labels = Train.cost.valuestrain = Train.drop ([' Quote_date ' , ' cost ', ' tube_assembly_id '], Axis = 1) # Convert data to NumPy Arraytrain = Np.array (train) test = Np.array (test)From:kaggle Copyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced. Ma