coursera 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

column of the BCW dataset before applying it to a linear classifier. In addition, we want to compress the original 30 dimension features into 2 dimensions, which is given to the PCA.Before we all performed an operation at each step, we now learn to connect Standardscaler, PCA, and logisticregression together using pipelines:The pipeline object receives a list of tuples as input, each tuple has the first value as the variable name, and the second element of the tuple is transformer or estimator

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

another feature of the library Numarray of the same nature, and added other extensions and developed the NumPy. NumPy is open source and co-maintained by many collaborators to develop.2 Matplotlib Brief IntroductionMatplotlib is a library of very similar MATLAB environments that generate publishing quality data. The user can output the data in a pop-up window as a raster format (PNG, TIFF, JPG) or as a vector file (e.g. EPS, PS). Matlab users are familiar with the graphics types and syntax for

, but please disregard its rationality)The branch of the decision tree for the two-value logic of "non-" is quite natural. In this data set, how is height and weight continuous value?Although this is a bit of a hassle, it's not a problem, it's just a matter of finding the intermediate points that divide these successive values into different intervals, which translates into two-value logic.The task of this decision tree is to find some critical values in height and weight, classify their sample

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

: Network Disk DownloadContent Introduction······Machine learning is one of the hottest areas in recent years, and the Python language has evolved into one of the mainstream programming languages over time. This book combines the two hot areas of machine learning and the

) Seeking a=x *θ (2) Ask E=g (A)-y(3) Request (A for step)3, algorithm optimization--stochastic gradient methodThe gradient rise (descent) algorithm needs to traverse the entire data set each time the regression coefficients are updated, which is good when dealing with about 100 datasets, but if there are billions of samples and thousands of features, the computational complexity of the method is too high. An improved method is to update the regression coefficients with only one sample point at

Environment SetupRust Generation WriteData Structure assginment Data structure generationMIPS Generation WritingMachine Learning Job WritingOracle/sql/postgresql/pig database Generation/Generation/CoachingWeb development, Web development, Web site jobsAsp. NET Web site developmentFinance insurace Statistics Statistics, regression, iterationProlog writeComputer Computational Method GenerationBecause of professional, so trustworthy. If necessary, pleas

(i) Understanding decision Trees1, decision tree Classification principleRecent surveys have shown that decision trees are also the most frequently used data mining algorithms, and the concept is simple. One of the most important reasons why a decision tree algorithm is so popular is that the user does not have to understand the machine learning algorithm, nor does it have to delve into how it works. Intuit

Environment:Win7 64-bit systemFirst step: install Python1, download python2.7.3 64-bit MSI version (here Select a lot of 2.7 of the other higher version resulting in the installation of Setuptools failure, do not know what the reason, for the time being, anyway, choose this version can be)2, install Python, all next point down.3, configure the environment variables, I am the default to add C:\Python path ca

is, the distribution statistics of the numbers appear, and are the result of normalization to the 0~1 interval. That is, the horizontal axis represents the number, and the vertical is the percentage of the number that corresponds to the horizontal axis in the 1000 random numbers. If you do not use the normalized horizontal axis for numbers (Normed=false), the vertical axis indicates the number of occurrences. If normalization is not used--the longitudinal axis indicates the number of oc

[Machine Learning Algorithm Implementation] Principal Component Analysis (PCA)-based on python + numpy, pcanumpy[Machine Learning Algorithm Implementation] Principal Component Analysis (PCA)-based on python + numpy @ Author: wepon

Spark Machine Learning Mllib Series 1 (for Python)--data type, vector, distributed matrix, API Key words: Local vector,labeled point,local matrix,distributed Matrix,rowmatrix,indexedrowmatrix,coordinatematrix, Blockmatrix.Mllib supports local vectors and matrices stored on single computers, and of course supports distributed matrices stored as RDD. An example of

At present, machine learning is one of the hottest technologies in the industry.With the rapid development of computer and network, machine learning plays a more and more important role in our life and work, and it is changing our life and work. From the daily use of the camera, daily use of the search engine, online e

" from NumPy Import *def loadsimpdata (): Datmat = Matrix ([[[1., 2.1], [2., 1.1], [1.3, 1.], [1., 1.], [2., 1.]]) Classlabels = [1.0, 1.0, -1.0, -1.0, 1.0] return datmat,classlabelsdef loaddataset (fileName): #general function to Parse tab-delimited Floats numfeat = Len (open (FileName). ReadLine (). Split (' \ t ')) #get number of fields Datamat = [ ]; Labelmat = [] fr = open (fileName) for line in Fr.readlines (): Linearr =[] curline = Line.strip (). Split (' \ t ') for I in

Tools used: NumPy and MatplotlibNumPy is the most basic Python programming library in the book. In addition to providing some advanced mathematical algorithms, it also has a very efficient vector and matrix operations function. These are particularly important for computational tasks for machine learning. Because both the characteristics of the data, or the batch

such as the followingHere is an example of a Python implementation:#-*-coding:cp936-*-"Created on Nov, 2010Adaboost was short for Adaptive Boosting@author:peter" from NumPy Import *def loadsimpdata (): Datmat = Matrix ([[[1., 2.1], [2., 1.1], [1.3, 1.], [1., 1.], [2., 1.]]) Classlabels = [1.0, 1.0, -1.0, -1.0, 1.0] return datmat,classlabelsdef loaddataset (fileName): #general function to Parse tab-delimited Floats numfeat = Len (open (File