Oreilly – Machine Learning with Python for Everyone, Part 2: Measuring Models 2022-9

Oreilly – Machine Learning with Python for Everyone, Part 2: Measuring Models 2022-9 Downloadly IRSpace

Apr 17, 2025 - 19:19 Updated: Aug 17, 2025 - 14:38

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Oreilly – Machine Learning with Python for Everyone, Part 2: Measuring Models 2022-9

Course Machine Learning with Python for Everyone Part 2: Measuring Models. You will become familiar with the basic criteria used to evaluate general learning systems and the specific criteria used in classification and regression. You will learn techniques to derive the most informative learning performance metrics from your data. You will have a powerful toolbox of numerical and graphical techniques to understand how your learning system performs on new data. This course is suitable for anyone who needs to improve their basic understanding of machine learning concepts and become familiar with basic machine learning code. You may be a newer data scientist, a data analyst transitioning to using machine learning models, an R&D scientist looking to add machine learning techniques to your classic statistical training, or a manager who needs Adds Data Science/Machine Learning to your team.

Students should have a basic understanding of programming in Python (variables, basic control flow, simple scripts). They should also be familiar with machine learning vocabulary (dataset, training set, test set, model), but knowledge about the concepts can be very shallow. They should have a working Python installation that allows you to use scikit-learn and matplotlib.

What you will learn in the course

Recognize misfits and overfits with graphical charts.
Use resampling techniques like cross-validation to get the most out of your data.
Graphical evaluation of learning performance of learning systems
Compare production learners with baseline models on different classification criteria
Construction and evaluation of confusion matrices and ROC curves
Apply classification criteria to multi-class learning problems
Create accurate recall and lift curves for classifiers
Compare production regression techniques with baseline regressions on different regression measures
Construct residual plots for the regressions