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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2.1.14.Decision trees and random forests

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This section contain the following items:

Introduction of tree methods
Decision trees and Random Forests with Python