Learn to explain interpretable and black box machine learning models with LIME, Shap, partial dependence plots, ALE plots, permutation feature importance and more, utilizing Python open source libraries..
Interpret models, build trust, and make confident decisions..
If you're disappointed for whatever reason, you'll get a full refund.
Sole is a lead data scientist, instructor, and developer of open source software. She created and maintains the Python library Feature-engine, which allows us to impute data, encode categorical variables, transform, create, and select features. Sole is also the author of the"Python Feature Engineering Cookbook," published by Packt.
Welcome to the most comprehensive online course on Interpretable Machine Learning.
In this course, you will learn methods and tools to interpret intrinsically explainable machine learning models like linear regression, decision trees, random forests and gradient boosting machines. And you will also discover methods to explain black-box algorithms, like deep neural networks, clustering methods, anomaly detection models and more.
In our Interpretable Machine Learning Course you will find detailed explanations of how the methods work, their advantages, the risks of using interpretable machine learning methods and how to implement these algorithms in Python.
Interpretability in machine learning refers to our ability to understand and explain how machine learning algorithms make predictions. It involves unraveling the inner workings of machine learning models to gain insights into their decision-making processes, or using alternative post-hoc methods to understand the output of more complex models.
Interpretable machine learning enables us to understand what are the main drivers of a model’s prediction, and also, why a model produced a particular prediction, providing transparency and accountability in AI systems.
In the context of machine learning, interpretability helps answer questions such as:
In the business context, interpreting our ML models allows us to understand if:
Interpretability has become an indispensable aspect of modern machine learning and artificial intelligence. Interpretable ML empowers organizations to deploy models they can understand and therefore trust.
Interpretations of machine learning models offer valuable insights that can be utilized in various ways. For example:
As the demand for explainable AI continues to rise across various industries, mastering interpretability techniques has become crucial for data scientists, researchers, and data professionals.
