where to start machine learning

Machine Learning Quick Start (2) Machine Learning Quick Start (2)-Classification Abstract: This article briefly describes how to use a classification algorithm to evaluate the bank's loan issuance model. Statement: (the content

line, but considering the following to be able to be in the command line convenient quick Start, recommended decompression to the/usr/lib/directory, I use the method is first decompression after removal, command as follows:TAR-XVF sublime\ text\ 2.0.1.tar.bz2MV Sublime\ text\ 2/usr/lib/where \ is the escape characterThis is done because the $PATH environment variable automatically covers the/usr/lib directory and does not specifically modify the envi

(file) # Open the previously saved code # File.close ()#或者自动关闭方案With open (' Pickle_exm.pickle ', ' RB ') as File:a_dic=pickle.load (file)30. Use set to find differentChar_list=[' A ', ' B ', ' C ', ' C ']print (set (char_list)) #使用set进行不同查找, output is a non-repeating sequence, sorted by hash sentence= ' Welcome to Shijiazhuang ' Print (set (sentence)) #可以分辨句子中的不同字母 and presented in a single form# 31, regular expressions (to be added)import Re #引入正则表达式pattern1 = "Cat" pattern2= ' dog ' string=

from:http://blog.csdn.net/lsldd/article/details/41551797In this series of articles, it is mentioned that the use of Python to start machine learning (3: Data fitting and generalized linear regression) refers to the regression algorithm for numerical prediction. The logistic regression algorithm is essentially regression, but it introduces logic functions to help

, 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

Machine learning process, in addition to Spyder,anaconda is also a kind of python, it is an open source release version, mainly for scientific computing. It seems to me that the main advantage is that many third-party libraries are pre-installed, and the Conda Install command is added to the anaconda, which is especially handy for installing new package, and bringing Spyder IDE and Jupyter notebook, etc. Do

Installing the Redis on a Linux machine is very easy, not to be introduced here. Since I have only one machine to learn from the master-slave copy function, I need to start multiple Redis instances on a single machine. We need to copy the default redis.conf file, and then modify the corresponding settings on it, to ens

Prediction problems in machine learning are usually divided into 2 categories: regression and classification .Simply put, regression is a predictive value, and classification is a label that classifies data.This article describes how to use Python for basic data fitting, and how to analyze the error of fitting results.This example uses a 2-time function with a random perturbation to generate 500 points, and

It is mentioned in this series that using Python to start machine learning (3: Data fitting and generalized linear regression) mentions the regression algorithm for numerical prediction. The logical regression algorithm is essentially regression, but it introduces a logical function to help classify it. The practice found that the logical regression in the field

[Introduction to machine learning] Li Hongyi Machine Learning notes-9 ("Hello World" of deep learning; exploring deep learning) PDF Video Keras Example application-handwriting Digit recognition Step 1

IntroductionThe systematic learning machine learning course has benefited me a lot, and I think it is necessary to understand some basic problems, such as the category of machine learning algorithms.Why do you say that? I admit that, as a beginner, may not be in the early st

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust