Introduction
Chapter 1.Notes from research
Chapter 2.Courses
- 2.1.Python for Data Science and Machine Learning Bootcamp
  2.1.1.Virtual Environment
  2.1.2.Python crash course
  2.1.3.Python for Data Analysis - NumPy
  2.1.3.1.Numpy arrays
  2.1.3.2.Numpy Array Indexing
  2.1.3.3.Numpy Operations
  2.1.4.Python for Data Analysis - Pandas
  2.1.4.1.Introduction
  2.1.4.2.Series
  2.1.4.3.DataFrames
  2.1.4.4.Missing Data
  2.1.4.5.GroupBy
  2.1.4.6.Merging joining and Concatenating
  2.1.4.7.Data input and output
  2.1.5.Python for Data Visual Visualization - Pandas Built-in Data Visualization
  2.1.6.Python for Data Visualization - Matplotlib
  2.1.6.1.Introduction of Matplotlib
  2.1.6.2.Matplotlib
  2.1.7.Python for Data Visualization - Seaborn
  2.1.7.1.Introduction to Seaborn
  2.1.7.2.Distribution Plots
  2.1.7.3.Categorical Plots
  2.1.7.4.Matrix Plots
  2.1.7.5.Grids
  2.1.7.6.Regression Plots
  2.1.8. Python for Data Visualization - Plotly and Cufflinks
  2.1.8.1.Introduction to Plotly and Cufflinks
  2.1.8.2.Plotly and Cufflinks
  2.1.9. Python for Data Visualization - Geographical plotting
  2.1.9.1.Choropleth Maps - USA
  2.1.9.2.Choropleth Maps - World
  2.1.10.Combine data analysis and visualization to tackle real world data sets
  911 calls capstone project
  2.1.11.Linear regression
  2.1.11.1.Introduction to Scikit-learn
  2.1.11.2.Linear regression with Python
  2.1.12.Logistic regression
  2.1.12.1.Logistic regression Theory
  2.1.12.2.Logistic regression with Python
  2.1.13.K Nearest Neighbors
  2.1.13.1.KNN Theory
  2.1.13.2.KNN with Python
  2.1.14.Decision trees and random forests
  2.1.14.1.Introduction of tree methods
  2.1.14.2.Decision trees and Random Forests with Python
  2.1.15.Support Vector Machines
  2.1.16.K means clustering
  2.1.17.Principal Component Analysis
- 2.2. Machine Learning Crash Course Jam

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2.2.2.Basic usage

Previous2.2.1.Introduction Next2.2.3.some common operations

Last updated 4 years ago

1.打開console, 輸入Ipython notebook

2.new->Python2, 打開新記事本

3.可在輸入欄下輸入pyhton指令, 並按執行可快速得到結果

4.輸入linux指令: Insert -> Insert Cell Bellow