Resources for learning about advanced topics

More on evaluation metrics: https://inria.github.io/scikit-learn-mooc/evaluation/evaluation_module_intro.html

Different machine learning models:

• In general: Scikit-learns amazing user guide: https://scikit-learn.org/stable/user_guide.html

• Understanding linear models: https://inria.github.io/scikit-learn-mooc/linear_models/linear_models_module_intro.html

• Understanding decision tree models: https://inria.github.io/scikit-learn-mooc/trees/trees_module_intro.html

• Understanding ensemble learning: https://inria.github.io/scikit-learn-mooc/ensemble/ensemble_module_intro.html

• Support Vector Machines: https://scikit-learn.org/stable/modules/svm.html

• Introduction to deep learning course (good for understanding DL and creating your own neural networks with Keras): carpentries-incubator/deep-learning-intro

• Fast.ai (Python library similar to sklearn, but applying state-of-the-art deep learning): https://docs.fast.ai/

Hyperparameter tuning: https://inria.github.io/scikit-learn-mooc/tuning/parameter_tuning_module_intro.html

Feature selection: https://inria.github.io/scikit-learn-mooc/feature_selection/feature_selection_module_intro.html

Feature importance: https://inria.github.io/scikit-learn-mooc/python_scripts/dev_features_importance.html