python for data science and machine learning bootcamp
python for data science and machine learning bootcamp
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Learning Data Science at the Command Line, Win7 under the installation environment is encountered some small problems, finally through the Baidu solution.1) After the computer installs the Vagrant+virtual box, the new working directory, CMD enters the working directory$ vagrant Init Data-
1.1 machine learning basics-python deep machine learning, 1.1-python
Refer to instructor Peng Liang's video tutorial: reprinted, please indicate the source and original instructor Peng Liang
Video tutorial: http://pan.baidu.com/s/
:15px "> learning R Blog URL: http://learnr.wordpress.com
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r home page: http://www.r-project.org
rstdio home page:/http/ www.rstdio.com/
r Introduction: http://www.cyclismo.org/tutorial/R/
r a relatively complete getting Started Guide: http://www.statmethods.net/about/sitemap.html
plyr Reference Document: Http://cran.r-projects.org/web/packages/plyr/plyr.pdf
ggplot2 Reference Document: Http
See original book 2.1-2.2 sectionThe new dataset is like a wrapped gift, filled with promise and hope!But until you open it, it remains mysterious!I. Structure and terminology of the underlying problem, characteristics of the machine learning data setTypically, rows represent instances, columns represent attribute characteristicsproperty, the
1, vagrant to establish a simple Httpserver method:1) Map PortModify the Vagrantfile, add the mappings for the local port and the VM port at the end, and then execute the vagrant reload. Vagrant::config.run do |config| # Forward Guest Port 8000 to host Port 8000 Config.vm.forward_port 8000, 8000 2) Start HttpserverWith Python's own Web server Simplehttpserver, enter the following command under a specific directory (establish a index.html) to start the Web server and provide a W
ProfileThis article is the first of a small experiment in machine learning using the Python programming language. The main contents are as follows:
Read data and clean data
Explore the characteristics of the input dat
Technology Research Center, Institute of Information Technology. Member of China Computer Society information Storage Technology Committee, senior member of China Computer Society, member of National Technical Commission for the Standardization of Documents Imaging Technology (SAC/TC86/SC6), IEEE member. Research areas include mass data storage, organization and management, analysis, and its applications in the Digital Library/Archives/education/medi
This article focuses on the contents of the 1.2Python libraries and functions in the first chapter of the Python machine learning time Guide. Learn the workflow of machine Learning.I. Acquisition and inspection of dataRequests getting dataPandans processing Data1 ImportOS2 ImportPandas as PD3 ImportRequests4 5PATH = R'
[Machine learning algorithm-python implementation] matrix denoising and normalization, python Machine Learning1. The background project is required. We plan to use python to implement matrix denoising and normalization. The numpy
Python Machine Learning Theory and Practice (6) Support Vector Machine and python Learning Theory
In the previous section, the theory of SVM is basically pushed down, and the goal of finding the maximum interval is finally convert
This article focuses on the contents of the 1.2Python libraries and functions in the first chapter of the Python Machine learning Time Guide. Learn the workflow of machine learning.I. Acquisition and inspection of dataRequests getting dataPandans processing Data1 ImportOS2 ImportPandas as PD3 ImportRequests4 5PATH = R'
article, I describe how to handle the system's own and installed Python versions.Python machine learning related librariesPythonThere are many libraries involved in machine learning, such as,,, and Theano TensorFlow PyTorch scikit-learn so on. Considering that scikit-learn
First, the foregoingNumPy(numerical python abbreviation) is an open source Python Scientific Computing Library. Use NumPy , you can use arrays and matrices in a very natural way . Numpy contains many useful mathematical functions, including linear algebra operations, Fourier transforms, and random number generation functions . The Library's predecessor was a library for array operations that began in 1995
For the performance of four different algorithms in different size data, it can be seen that with the increase of data volume, the performance of the algorithm tends to be close. That is, no matter how bad the algorithm, the amount of data is very large, the algorithm can perform well.When the amount of data is large,
Python machine learning decision tree and python machine Decision Tree
Decision tree (DTs) is an unsupervised learning method for classification and regression.
Advantages: low computing complexity, easy to understand output resul
Use Python to implement machine awareness (python Machine Learning 1 ).0x01 Sensor
A sensor is a linear classifier of the second-class Classification and belongs to a discriminant model (another is to generate a model ). Simply put, the objective is divided into two categori
Machine learning practices in python3.x and python machine learning practices
Machine Learning Practice this book is written in the python2.x environment, while many functions and 2 in
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