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Explain what drives your model and each prediction using regression coefficients, odds ratios and feature importance
This course provides a practical grounding in machine learning interpretability, combining the core concepts with extensive hands-on work in Python. It is designed for data professionals who need to explain how their models make predictions, both overall and for individual cases.
The course opens with a conceptual introduction to interpretability. You will understand what interpretability is and why it matters, the difference between global and local explanations, the main families of interpretability methods, and the challenges of explaining models in practice, including the role of interpretability in detecting bias and supporting fair, responsible use of models in regulated industries.
The second and third sections are hands-on deep dives into linear and logistic regression using Python and scikit-learn. In the linear regression section, you will fit models, evaluate how well they fit the data, interpret coefficients, detect and handle multicollinearity, and use Lasso regularization to select features and build models that are both accurate and easy to explain. In the logistic regression section, you will apply the same approach to classification, learning how the model estimates probabilities and how to interpret its coefficients and individual predictions.
By the end of the course, you will be able to build, evaluate and explain linear and logistic regression models in Python, and communicate their behavior clearly to stakeholders.
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Data Scientist | Python Developer | Author and Instructor
I'm a data scientist, machine learning educator and open-source developer. I've built machine learning models for credit risk, insurance claims and fraud prevention, and I am passionate about helping data scientists build models that hold up in real-world projects. My courses are designed for intermediate and advanced practitioners. They cover feature engineering, feature selection, hyperparameter optimization, imbalanced data and the design of robust machine learning pipelines, with a strong emphasis on techniques you can apply straight away in your own work. In the age of generative AI, when a working model can be coded in minutes, the real skill lies in understanding the methods deeply enough to review that output with rigor and a critical eye, and that's exactly what these courses are built to develop. I'm the creator and maintainer of Feature-engine, an open-source Python library for feature engineering and feature selection used by data scientists worldwide. I'm also the author of three books published by Packt: Python Feature Engineering Cookbook, Feature Selection in Machine Learning, and Imbalanced Data: Myths, Mistakes and Modern Solutions. I speak regularly at conferences and meetups, and I enjoy connecting technical communities with the tools and knowledge they need to succeed. In 2018 I received a Data Science Leaders Award, and in 2019 LinkedIn recognized me as one of its voices in data science and analytics. Before moving into data science, I earned an MSc in Biology and a PhD in Biochemistry, then spent more than eight years as a research scientist at institutions including University College London and the Max Planck Institute. That scientific training still shapes how I teach: rigorous, evidence-based, and focused on understanding why a method works before reaching for it.