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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4.1.Introduction

Previous4. Web crawler Next4.2.requests

Last updated 4 years ago

網路爬蟲系統可分三部分:
- web connector: 負責對website發出request以及接受及response
  - requests module
- data parser: 負責分析web connector所收到的response, 得到有用的information
  - beautifulsoup4 module
- data center: 將分析所得的information儲存到database