text: # See also examples/example_track/example_meta.py for a longer, commented example
  track = dict(
      author_username='dansbecker',
      course_name='Machine Learning Explainability',
      course_url='https://www.kaggle.com/learn/machine-learning-explainability'
  )
  
  lessons = [ {'topic': topic_name} for topic_name in
                      ['Use Cases for Model Insights',
                      'Permutation Importance',
                      'Partial Plots',
                      'SHAP Values',
                      'Advanced Uses of SHAP Values'
                      ]
              ]
  
  notebooks = [
      dict(
          filename='tut1_intro.ipynb',
          lesson_idx=0,
          type='tutorial',
          ),
      dict(
          filename='tut2_perm_importance.ipynb',
          lesson_idx=1,
          type='tutorial',
          ),
      dict(
          filename='ex2_perm_importance.ipynb',
          lesson_idx=1,
          type='exercise',
          scriptid=1637562
      ),
      dict(
          filename='tut3_partial_plots.ipynb',
          lesson_idx=2,
          type='tutorial'
      ),
      dict(
          filename='ex3_partial_plots.ipynb',
          lesson_idx=2,
          type='exercise',
          scriptid=1637380
      ),
      dict(
          filename='tut4_shap_basic.ipynb',
          lesson_idx=3,
          type='tutorial'
      ),
      dict(
          filename='ex4_shap_basic.ipynb',
          lesson_idx=3,
          type='exercise',
          scriptid=1637226
      ),
      dict(
          filename='tut5_shap_advanced.ipynb',
          lesson_idx=4,
          type='tutorial'
          ),
      dict(
          filename='ex5_shap_advanced.ipynb',
          lesson_idx=4,
          type='exercise',
          scriptid=1699743
          )
  ]
  
  for nb in notebooks:
      nb['dataset_sources'] =  [
      "mathan/fifa-2018-match-statistics",
      "dansbecker/hospital-readmissions",
      "dansbecker/new-york-city-taxi-fare-prediction",
    ]
  
  