andrew ng machine learning python

linear algebra and similar to numpy arrays.DecafDecaf is a recent deep learning library published by UC Berkeley, tested in the Imagenet Classification challenge, and its neural network implementation is very advanced (state of art).NolearnIf you want to use the excellent Scikit-learn Library API in deep learning, encapsulating the decaf Nolearn will make it easier for you to use it. It is the packaging fo

weight, so that the nearest neighbor's weight is far greater than the neighbor's weights), the Gaussian function (or other appropriate subtraction function) calculation weight = Gaussian (distance) (The farther away you get the smaller the value, the more accurate the weighted estimate.)(v) SummaryThe K-nearest neighbor algorithm is the simplest and most efficient algorithm for classifying data, and its learning is based on the example, we must have

Prepare for machine learning using Python The machine learning getting started book "Machine Learning Practice" uses the python language. Th

previous article Python machine learning "Getting Started"Body:In the previous introductory article, we mainly introduced two algorithms for machine learning tasks: supervised learning and unsupervised

1. Background When I was outside the company internship, a great God told me that learning computer is to a Bayesian formula applied to apply. Well, it's finally used. Naive Bayesian classifier is said to be a lot of anti-Vice software used in the algorithm, Bayesian formula is also relatively simple, the university to do probability problems often used. The core idea is to find out the most likely effect of the eigenvalue on the result. The formula

three sheets; train_set.csv;test_set.csv;feature.csv. Three tables are associated by object_id.Copyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced.Machine learning in Python: Merging multiple tables based on keywords (building a combined feature)

1. Confusion Matrixis a confusion matrix of two types of problems in which the output uses a different category labelCommonly used metrics to measure classification performance are: The correct rate (Precision), which is equal to tp/(TP+FP), gives the ratio of the true positive example in the sample that is predicted to be a positive example. recall Rate (Recall), which he equals to tp/(TP+FN), gives the true positive example of the predicted positive example as the proportion of al

introductionThe basic SVM classifier solves the problem of the 2 classification, the case of N classification has many ways, here is introduced 1vs (n–1) and 1v1. More SVM Multi-classification application introduction, reference ' SVM Multi-Class classification method 'In the previous method we need to train n classifiers, and the first classifier is to determine whether the new data belongs to the classification I or to its complement (except for the N-1 classification of i). The latter way we

The algorithm we learned today is the KNN nearest neighbor algorithm. KNN is an algorithm for supervised learning classifier classification. Next we will discuss in detail Preface I recently started to learn machine learning. I found a book about machine learning on the Int

The idea of clustering: dividing a DataSet into several subsets (called a cluster cluster) that you don't want to cross, each potentially corresponding to a concept. But the practical significance of each cluster is determined by the users themselves, and the clustering algorithm will only be divided.The role of Clustering:1) can be used as a separate process for finding a distribution pattern of data2) as a preprocessing process for classification. First, classify data is clustered and then the

[:, 1].max () + 1, 0.005 +grid_x =Np.meshgrid (Np.arange (L, R, h), A Np.arange (b, T, v)) atflat_x = Np.c_[grid_x[0].ravel (), grid_x[1].ravel ()] -Flat_y =model.predict (flat_x) -Grid_y =Flat_y.reshape (Grid_x[0].shape) -Mp.figure ('Logistic Classification', -Facecolor='Lightgray') -Mp.title ('Logistic Classification', fontsize=20) inMp.xlabel ('x', fontsize=14) -Mp.ylabel ('y', fontsize=14) toMp.tick_params (labelsize=10) +Mp.pcolormesh (Grid_x[0], grid_x[1], grid_y, cmap='Gray') -Mp.scatter

1. Background Decision Book algorithm is a kind of classification algorithm approximating discrete numbers, which is simpler and more accurate. International authoritative academic organization, Data Mining International conference ICDM (the IEEE International Conference on Data Mining) in December 2006, selected the ten classical algorithms in the field of mining, C4.5 algorithm ranked first. C4.5 algorithm is a kind of classification decision tree algorithm in

can be obtained through the best_score_ attribute, and the specific parameter information can be obtained through the Best_params_ attribute.Selecting algorithms by nested cross-validationCombined with the grid search for K-fold cross-validation, it is an effective way to improve the performance of machine learning model by optimizing the machine

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