To get the most out of this book

In order to follow the instructions given in this book, you will need basic knowledge of the following:

• Access to Python via Integrated Development Environments (IDE), Jupyter notebook, or Colab notebook.
• Basics of Python programming.
• Basic understanding of machine learning modeling and terminologies, such as supervised learning, unsupervised learning, and model training and testing.

Having a virtual environment with all the required libraries would help you to run the code in each chapter, which is provided as Jupyter notebooks in the associated GitHub repository of the book.

The Python libraries required for the book are: sklearn >= 1.2.2, numpy >= 1.22.4, pandas >= 1.4.4, matplotlib >= 3.5.3, collections >= 3.8.16, xgboost >= 1.7.5, sklearn >= 1.2.2, ray >= 2.3.1, tune_sklearn >= 0.4.5, bayesian_optimization >= 1.4.2, imblearn, pytest >= 7.2.2, shap >= 0.41.0, aif360 >= 0.5.0, fairlearn >= 0.8.0, pytest >= 3.6.4, ipytest >= 0.13.0, mlflow >= 2.1.1, libi_detect >= 0.11.1, lightgbm >= 3.3.5, evidently >= 0.2.8, torch >= 2.0.0, torchvision >= 0.15.1, transformers >= 4.28.0, datasets >= 2.12.0, torch_geometric == 2.3.1, dowhy == 0.5.1, bnlearn == 0.7.16, tenseal >= 0.3.14, pycryptodome = 3.18.0, pycryptodomex = 3.18.0

Alternatively, you can use online services, such as Colab, and run the notebooks as Colab notebooks.

Importing the required libraries is omitted for every single code cell to eliminate the repetition and keep the book as short as possible. Having the GitHub repository of the book on the side will help you to be sure about the required libraries for each piece of code and learn how to install them. As this book is not a single command tutorial book, the majority of the examples include multi-line processes. As a result, you cannot copy-paste individual lines, in most chapters, without paying attention to the required libraries, their installation, and the code lines before that.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.