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The Complete SHAP and Shapley Values Masterclass
Published 9/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 48m | Size: 1.07 GB
Understand SHAP values in depth to explain machine learning models accurately, critically and with confidence.
What you'll learn
Explain the intuition behind Shapley values and how they attribute model predictions
Calculate Shapley values correctly for machine learning models
Interpret local and global SHAP values, outputs, and visualizations
Use the SHAP Python library to explain model predictions
Evaluate SHAP's assumptions and limitations and decide when it is appropriate
Requirements
Basic knowledge of Python and common machine learning workflows
Familiarity with model training, predictions, and feature importance
Some experience with pandas and scikit-learn
Description
Welcome toThe Complete SHAP and Shapley Values Masterclass, a focused, practical course for understanding how SHAP explains machine learning models.
SHAP is the most popular tool for machine learning interpretability. Yet, many use it blindly, without understanding how it actually models the contributions of the features to the predictions.
We'll begin by building an intuitive understanding of Shapley values: what they are, where they come from, and how they distribute a prediction among the model's input features.
We'll then examine how Shapley values are calculated for machine learning models. You'll understand what is being modelled during this calculation, how different choices influence the resulting values, and what SHAP can, and cannot tell you about a model's predictions.
Through clear explanations and Python demonstrations, you will learn how to
- Understand the intuition behind Shapley values
- Calculate Shapley values for machine learning predictions
- Identify what SHAP values are actually modelling
- Understand how the calculation affects the resulting explanations
- Interpret local and global SHAP outputs correctly
- Recognize what SHAP explanations reveal about model predictions
- Identify assumptions, limitations, and potentially misleading interpretations
- Use the SHAP Python library and understand its outputs
- Decide whether SHAP is appropriate for a particular model and problem
This course is designed for data scientists, machine learning practitioners, analysts, and technical professionals who want to interpret models with greater confidence.
Every concept is supported by Python examples that connect the theory to practical model interpretation.
SHAP values are not a silver bullet, and using them responsibly requires more than importing a library and producing a plot.
By the end of the course, you will be able to use SHAP with a clear understanding of how its explanations are produced, what they mean, and when they should, or should not be trusted.
Who this course is for
Data scientists and machine learning practitioners who want to understand SHAP beyond generating plots
Python users who need to explain model predictions accurately and responsibly
Professionals who must assess whether SHAP is suitable for their models and business problems
AI coding-agent users who want to evaluate and validate generated model-interpretability workflows
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