second edition with code snippets in Python and R
This book is a unique entanglement of theory, examples and processes relevant to Responsible Machine Learning. You will find intuitions and examples for Interpretable Machine Learning (IML) and eXplainable Artificial Intelligence (XAI). Descriptions are supplemented by code snippets with examples for Python and R with the use of DALEX package. Finally, the process is shown through a comic book describing the adventures of two characters, Beta and Bit. The interaction of these two shows the decisions that analysts often face, whether to try a different model, try another technique for exploration or look for other data -- questions like how to compare models or validate them.
All examples are fully reproducible so that one can replay all adventures on a local desktop.
Model development is a responsible and challenging task but also an exciting adventure. Sometimes textbooks focus only on the technical side, losing all the fun. Here we are going to have it all.
- Free flipbook: https://RML.mi2.ai
- Data: R/covid_spring.csv
- Data: R/covid_summer.csv
- Data: Python/py_covid_spring.csv
- Data: Python/py_covid_summer.csv
Please, note that these are artificial datasets generated to mimic relations present in the real data.
- Polish: Wprowadzenie do Modelowania Predykcyjnego GitHub, flipbook
- Spanish: La Guía del Viajero al Aprendizaje Automático Responsable GitHub, flipbook
- German: Per Anhalter durch die Galaxis des verantwortungsvollen maschinellen Lernens flipbook, paperback
- Turkish: Sorumlu Makine Öğrenmesi Rehberi GitHub, flipbook
- Vietnamese: 4.0.1 Cùng xây dựng Model Machine Learning với Bêta và Bít GitHub, flipbook