Python data visualization, data mining, machine learning, deep learning common libraries, IDES, etc.

Source: Internet
Author: User
Tags continuum analytics



First, the visualization method


    1. Bar chart
    2. Pie chart
    3. Box-line Diagram (box chart)
    4. Bubble chart
    5. Histogram
    6. Kernel density estimation (KDE) diagram
    7. Line Surface Chart
    8. Network Diagram
    9. Scatter chart
    10. Tree Chart
    11. Violin chart
    12. Square Chart
    13. Three-dimensional diagram


Second, interactive tools


    1. Ipython, Ipython Notebook
    2. plotly


Iii. Python IDE Type


    1. Pycharm, specifying a Java swing-based user interface
    2. PyDev, SWT-based user interface (for Eclipse)
    3. IEP (Interactive Editor for Pyhton), interactive editor
    4. Canopy in Enthought: based on PYQT
    5. Anaconda release of the Spyder in Continuum Analytics: PYQT-based


Iv. Interactive Visualization software package


    1. D3.js
    2. Bokeh
    3. Vispy
    4. Wakari
    5. Ashiba


V. Statistics and data Mining algorithms


  1. hmm-Hidden Markov model


  2. viterbi-Viterbi algorithm


  3. Logistic regression


  4. Decision Tree: ID3, C4.5, CART---classification


  5. K-means (k-means)---clustering algorithm


  6. SVM Support Vector Machine---supervised learning, statistical classification and regression analysis


  7. Maximum expectation (EM) algorithm


  8. PageRank


  9. AdaBoost


  10. KNN (k-Neighbor algorithm)


  11. Bayesian classifier, naive Bayesian


  12. Random Forest


  13. LDA Thematic model






Vi. Deep Learning


    1. CNN convolutional Neural Network
    2. RNN Cyclic Neural network


Python data visualization, data mining, machine learning, deep learning common libraries, IDES, etc.


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