Notebook Difficulty Level: ★★★☆☆
Through the set of 4 Jupyter Notebooks (referred on the list below) that are part of our “Rock, Paper or Scissor Classification Game” a reasonable understanding of the different Machine Learning stages, that need to be followed in order to train an effective classification system, can be reached.
Previous Notebooks that are part of “Rock, Paper or Scissor Game – Train and Classify” module
- Rock, Paper or Scissor Game – Train and Classify [Volume 1] | Experimental Setup
- Rock, Paper or Scissor Game – Train and Classify [Volume 2] | Feature Extraction
- Rock, Paper or Scissor Game – Train and Classify [Volume 3] | Train and Classify
- Rock, Paper or Scissor Game – Train and Classify [Volume 4] | Performance Evaluation
All the previous Jupyter Notebooks are focused on the application of scikit-learn ( Python ) functionalities.
However Anaconda toolbox (presented at Download, Install and Execute Anaconda ) includes a very intuitive and graphical resource called Orange , that can be an interesting tool to complement our 4 volumes of “Rock, Paper or Scissor Game – Train and Classify” module
On the current Jupyter Notebook it will be done a very quick presentation of Orange
