data science from scratch first principles with python

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Data Science from Scratch's MapReduce

MapreduceMapReduce is a computational model, except that the computational model is in the world of parallel computing.Consider a simple example-word statisticsfrom collections import Counterimport redocuments = ["data science", "big data", "science fiction"]def tokenize(message): message = message.lower() all_wo

Comprehensive learning path–data Science in Python deep learning path-Learn with Python data

http://blog.csdn.net/pipisorry/article/details/44245575A very good article on how to learn python and use Python for data science, data analysis, machine learning Comprehensive learning Path–data

Wang Jialin's 11th lecture on hadoop graphic training course: Analysis of the Principles, mechanisms, and flowcharts of mapreduce in "the path to a practical master of cloud computing distributed Big Data hadoop-from scratch"

This section mainly analyzes the principles and processes of mapreduce. Complete release directory of "cloud computing distributed Big Data hadoop hands-on" Cloud computing distributed Big Data practical technology hadoop exchange group:312494188Cloud computing practices will be released in the group every day. welcome to join us! You must at least know

Python is a simple getting started tutorial for data science and python getting started tutorial

Python is a simple getting started tutorial for data science and python getting started tutorial Python has an extremely rich and stable data science tool environment. Unfortunately, fo

10 most popular machine learning and data Science python libraries

2018 will be a year of rapid growth in AI and machine learning, experts say: Compared to Python is more grounded than Java, and naturally becomes the preferred language for machine learningIn data science, Python's grammar is the closest to mathematical grammar, making it the easiest language for professionals such as mathematicians or economists to understand an

R VS Python in Data science: The winner is ...

R VS Python in Data science: The winner is ...In the "Best" data Science tools game, R and Python have their own pros and cons. The choice between the two depends on the use of the background, the need to learn spending and other

(Data Science Learning Codex 20) Derivation of principal component Analysis principle &python self-programmed function realization

main component from the largest contribution rate, until the cumulative contribution rate to meet the requirements;Then define the principal component load (loadings, which is called the factor load in the factor analysis):That is, the correlation coefficients of the first principal component and the J Primitive variable, the matrix a= (AIJ) is called the factor load matrix, and in practice the AIJ is used instead of Uij As the principal component coefficient, because it is a standardized coef

Comprehensive learning Path–data Science in Python

http://blog.csdn.net/pipisorry/article/details/44245575A good article on how to learn python and use Python for data science, data analysis, and machine learning Comprehensive(integrated) Learning Path–data

Python's easy-to-start tutorial on data science work

Python has an extremely rich and stable data science tool environment. Unfortunately, for those who do not know the environment is like a jungle (cue snake joke). In this article, I will step by step guide you how to get into this pydata jungle. You might ask, how about a lot of the existing Pydata package recommendation lists? I think it would be unbearable for

A simple introductory tutorial on the work of data science in Python _python

Python has an extremely rich and stable data science tool environment. Unfortunately, for those who do not know this environment is like a jungle (cue snake joke). In this article, I'll guide you step-by-step through how to get into this pydata jungle. You might ask, what about many of the existing Pydata package referral lists? I think it would be too much for

(Data Science Learning Codex 23) Decision tree Classification principle detailed &python and R implementation

arguments are missing samples (decision tree is more tolerant of missing values, there are corresponding processing methods)Parms: The default is the "Gini" index, which is the method of the CART decision tree Partition node;> Rm (list=ls ())>Library (Rpart.plot)>Library (Rpart)>data (Iris)> Data Iris> Sam 1: Max, -)> Train_data Data[sam,]> Test_data Sam,]> Dtre

Intermediate of Learning Notes Python for Data Science | Datacamp

Intermediate Python for Data Science | Datacamp Https://www.datacamp.com/courses/intermediate-python-for-data-science The intermediate Python course is crucial to your

Data Science Manual (R+python) reference information URL

:15px "> learning R Blog URL: http://learnr.wordpress.com p26_27 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://cran.r-project.org/web/packages/ggplot2/gg

The Python Data Science problem Rollup __python

Python has become increasingly popular among data science enthusiasts, and it is important that it brings a complete system to the universal programming language. With Python you can not only transform operational data, but also create powerful piping commands and machine le

(Data Science Learning Codex) a detailed introduction to the RE module in Python

First, IntroductionAs for regular expressions, I have already made a detailed introduction in the previous (Data Science Learning Codex 31), which summarizes the common functions of the self-contained module re in Python.As a module supported by Python for regular expression related functions, re provides a series of methods to complete the processing of almost a

Data processing and programming example of the python series from scratch (2)

This article mainly introduces python data processing and programming instances. For more information, see the previous example of data processing and programming in the python series from scratch (I, in addition to the student's score, the file adds the student's name and b

Data processing and programming example of the python series from scratch (2)

In the previous section, the data changes based on the data processing programming instance (I) of the python series from scratch. In addition to the student's score, the file added the Student name and birth date information, therefore, it will be changed to: output the first three best scores and the Year of birth fo

Principles and implementation of data Normalization (normalization) (Python Sklearn)

principleData Normalization (normalization) is a vector that transforms each sample (vector) of data into a unit norm, each of which is independent of each other. In effect, each component value in the vector is divided by the normalization factor. Common regularization factors are L1, L2, and Max. Suppose, for a vector of length n, the formula of its regularization factor Z, as follows:Note: Max is different from infinity norm in that the infinity no

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