python machine learning bootcamp

(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

" 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

[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

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

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

Keras is a python library for deep learning that contains efficient numerical libraries Theano and TensorFlow. The purpose of this article is to learn how to load data from CSV and make it available for keras use, how to model the data of multi-class classification using neural network, and how to use Scikit-learn to evaluate Keras neural network models.Preface, the concept description of two classificatio

clustering are generally relatively random, generally not very ideal, and the final result tends to be indistinguishable from natural clusters, in order to avoid this problem, the binary K mean clustering algorithm is used in this paper .The implementation of the binary K-means clustering Python is given in the next blog post.Complete code and test data can be obtained here, or you want to get the source from the connection, because the copy code fro

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

Common Python machine learning packagesNumpy: A package for scientific computingPandas: Provides high-performance, easy-to-use data structures and data analysis toolsSCIPY: Software for math, science and engineeringStatsmodels: Used to explore data, estimate statistical models, statistical testsScikit-learn: Provides classic